The Pelagic Habitat of Swordfish (Xiphias gladius) in the Changing Environment of the North Indian Ocean

Elepathage, Thushani Suleka Madhubha; Tang, Danling; Oey, Lie Yauw

doi:10.3390/su11247070

Cited by 2 publications

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Candidate GAMs were constructed to model estuarine finfish monthly and yearly distributions and for exploring factors potentially influencing finfish species in the coastal region of the central Great Barrier Reef. Preferred models were selected according to the highest R 2 (adj) value, the lowest Akaike information criterion (AIC) value, generalised cross-validation (GCV) value and the diagnostic plots (residuals distribution plot, quantile-quantile plot and response v. fitted values plot) (Elepathage et al 2019). We focused the analysis on the following three species of commercially valuable finfish on the central Queensland coast: barramundi, red-throat emperor and blue threadfin salmon.…”

Section: Gam Analysismentioning

confidence: 99%

Predicting the current and future suitable-habitat distribution of tropical adult and juvenile targeted fishes in multi-sector fisheries of central Queensland, Australia

2023

View full text Add to dashboard Cite

Context Coastal and estuarine finfish species are responding to human-induced climate change by altering their distributions. In tropical regions, the species mostly affected by warming have limited acclimation capacity or live close to their upper thermal limits. Consequently, coastal fish assemblages may dramatically contract in range, experience declining population abundance or local extinction. Aim Here we use two different predictive modelling techniques that cope with non-linear empirical relationships between responses and environmental predictors to investigate distribution change. Methods The habitat-suitability models we use are the maximum entropy model (MaxEnt) and the generalised additive model (GAM). We built the models for the period 2004–2019 with environmental data relevant to coastal systems. We incorporated climate change at current conditions, near future (2015–2054) and distant future (2055–2100) from CMIP6 climate models. Key results We identified bathymetry and sea-surface temperature to be key variables explaining the current and future distribution of coastal finfish and elasmobranchs of the Great Barrier Reef coast in central Queensland. Conclusions We showed how the distributions of valuable fisheries species will change under future warming conditions. Implications The objective is to inform fisheries management supporting the restructure of existing fisheries or the development of new resources for the dual purposes of conservation and food security.

show abstract

Section: Gam Analysismentioning

confidence: 99%

Predicting the current and future suitable-habitat distribution of tropical adult and juvenile targeted fishes in multi-sector fisheries of central Queensland, Australia

2023

View full text Add to dashboard Cite

show abstract

Modeling the importance of subsurface environmental variables in driving swordfish (Xiphias gladius) catchability in the Western Indian Ocean

Tang,

Wang,

et al. 2024

Fisheries Oceanography

View full text Add to dashboard Cite

Many oceanic species in pelagic habitats move vertically through the water column, highlighting the ecological importance of that spatial environment for modeling habitats of marine species. The role and importance of multiple oceanic subsurface environmental variables in modeling the habitat suitability of swordfish (Xiphias gladius), a highly migratory large pelagic fish, is poorly understood. In this study, we analyzed adult swordfish data from the 2017–2019 Chinese Indian Ocean tuna longline fishery observer. We used the maximum entropy model (MaxEnt) and random forest model (RF) to compare modeling schemes that included multiple subsurface environmental datasets. The area under receiver operating characteristic curve (AUC) from training and test sets was evaluated to investigate whether the inclusion of subsurface variables could enhance model performance and affect the simulation results. This analysis showed that model performance was significantly enhanced after addition of subsurface environmental variables, and the best model fit was achieved at 200–300 m depth. Sea water temperature, dissolved oxygen, net primary production, and ocean mixed layer depth were the critical environmental factors constituting catchability for swordfish in the Western Indian Ocean. As the depth increased, dissolved oxygen became the most important environmental factor, replacing surface temperature. Compared with the surface model, the location and extent of areas of high catchability in certain months changed significantly after the addition of subsurface variables. The results of this study provide evidence for a better understanding of the selection of critical environmental variables and improvement of model performance in 3D habitat modeling of pelagic fish.

show abstract

The Pelagic Habitat of Swordfish (Xiphias gladius) in the Changing Environment of the North Indian Ocean

Cited by 2 publications

References 48 publications

Predicting the current and future suitable-habitat distribution of tropical adult and juvenile targeted fishes in multi-sector fisheries of central Queensland, Australia

Predicting the current and future suitable-habitat distribution of tropical adult and juvenile targeted fishes in multi-sector fisheries of central Queensland, Australia

Modeling the importance of subsurface environmental variables in driving swordfish (Xiphias gladius) catchability in the Western Indian Ocean

Contact Info

Product

Resources

About