Using a new framework of two-phase generalized additive models to incorporate prey abundance in spatial distribution models of juvenile slender lizardfish in Haizhou Bay, China

Xue, Ying; Tanaka, Kisei R.; Yu, Hongbin; Chen, Yong; Guan, Lisha; Li, Zengguang; Yu, Haiqing; Xu, Binduo; Ren, Yiping; Wu, Rong

doi:10.1080/17451000.2018.1447673

Cited by 20 publications

(9 citation statements)

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“…Depth and strata are another two important factors influencing the distribution of whitespotted conger. A series of similar studies have interpreted that depth and spatial factor might be present as the representatives of other influencing factors rather than directly affecting the distribution of marine fishes as determining factors themselves (Giannoulaki et al, ; Xue et al, ). Depth and strata are practically composite factors that incorporate a variety of correlated variables whose impacts on the distribution of fishes are sometimes not significant but should not be neglected.…”

Section: Discussionmentioning

confidence: 99%

“…This result could also be found in the comparison between HGLMs with strata as a spatial random effect and GLMMs without it in this study. The abundance of whitespotted conger might be not responding to these composite variables, but to some other hydrogeological (such as dissolved oxygen and sediment) and biotic factors, which would be explored in our future studies (Xue et al, ; Zhu et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…The population trend and spatial distribution of marine species are influenced not only by the physical environmental variables, but also by the quality and quantity of their prey as important drivers (Xue et al, ). The impacts of the other potential environmental variables (e.g., sediment type, water transparency, chlorophyll content) and the prey availability on the variation of conger abundance and distribution were not included in this study due to lack of survey and data.…”

Section: Discussionmentioning

confidence: 99%

“…The number of sampling sites in each stratum was proportional to its area, with a minimum of three sites being sampled per stratum. The overall survey sites during 2011–2017 basically covered Haizhou Bay (Li, Ye, et al, ; Xu, Zhang, Xue, Ren, & Chen, ; Xue et al, ). The survey vessels are otter trawl ship of 161.8 kW, with a trawl net of 25 m width and cod‐end mesh size of 17 mm.…”

Section: Methodsmentioning

confidence: 99%

“…In the present study, Pearson's correlation analysis was first conducted between explanatory variables to avoid multicollinearity problems before model building and evaluation. One of the explanatory variables was excluded if there was a significant correlation (|r| > 0.6) between any two variables (Li, Cao, Chang, Wilson, & Chen, 2015;Xue et al, 2018).…”

Section: Preliminary Explanatory-variable Selectionmentioning

confidence: 99%

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Population status and distribution of whitespotted conger (Conger myriaster) in Yellow Sea: An important migratory species along coastal China with limited data

Jiao

et al. 2019

Fisheries Oceanography

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Whitespotted conger (Conger myriaster) is a marine migratory species with important economic and ecological values in Northwestern Pacific. The population status and distribution of whitespotted conger may have changed in the eastern coastal water of China owing to the intensive fishing pressure and human activities in the past few decades. There have been miscellaneous concerns about its population trend and potential environment‐driven variations in spatiotemporal distribution. Delta‐generalized linear models, generalized linear mixed models, and a set of correlation analyses were performed to answer the above concerns, and a stratified random sampling‐designed bottom trawl survey from 2011 to 2017 in Haizhou Bay of the central Yellow Sea was conducted to collect samples. Our study suggested that whitespotted conger remained a relatively stable population status with slight fluctuations likely caused by climate variability during 2011–2017. The hierarchical generalized linear mixed models with spatial random effect performed better than the generalized linear model and the common generalized linear mixed models. Spatiotemporal differences of whitespotted conger were significantly associated with bottom water salinity, depth, sampling time, and space, which were probably mainly influenced by a combination of the Cold Water Mass, the Yellow Sea Warm Current, and the Yellow Sea Coastal Current. The predicted spatiotemporal distribution of whitespotted conger has a monthly variation matching its seasonable migration and an overall higher abundance in the deeper water than that in the neritic areas in Haizhou Bay.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Preliminary Explanatory-variable Selectionmentioning

confidence: 99%

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Population status and distribution of whitespotted conger (Conger myriaster) in Yellow Sea: An important migratory species along coastal China with limited data

Jiao

et al. 2019

Fisheries Oceanography

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Machine learning highlights the importance of primary and secondary production in determining habitat for marine fish and macroinvertebrates

Friedland

Bachman²,

Davies

et al. 2021

Aquatic Conservation

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Species distribution models for marine organisms are increasingly used for a range of applications, including spatial planning, conservation, and fisheries management. These models have been constructed using a variety of mathematical forms and drawing on both physical and biological independent variables; however, what might be called first‐generation models have mainly followed the form of linear models, or smoothing splines, informed by data collected in the context of fish surveys. The performance of different classes of variables were tested in a series of species occurrence models built with machine learning methods, specifically evaluating the potential contribution of lower trophic level data. Random forest models were fitted based on the classification of the absence/presence for fish and macroinvertebrates surveyed on the US Northeast Continental Shelf. The potential variables included physical, primary production, secondary production, and terrain variables. For accepted model fits, six variable importance measures were computed, which collectively showed that physical and secondary production variables make the greatest contribution across all models. In contrast, terrain variables made the least contribution to these models. Multivariable analyses that account for all performance measures reinforce the role of water depth and temperature in defining species presence and absence; however, chlorophyll concentration and some specific zooplankton taxa, such as Metridia lucens and Paracalanus parvus, also make important contributions with strong seasonal variations. Our results suggest that lower trophic level variables, if available, are valuable in the creation of species distribution models for marine organisms.

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Predicting the distribution of foraging seabirds during a period of heightened environmental variability

Evans

Lea

Hindell

2021

Ecological Applications

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Quantifying the links between the marine environment, prey occurrence, and predator distribution is the first step towards identifying areas of biological importance for marine spatial planning. Events such as marine heatwaves result in an anomalous change in the physical environment, which can lead to shifts in the structure, biomass, and distribution of lower trophic levels. As central‐place foragers, seabirds are vulnerable to changes in their foraging grounds during the breeding season. We first quantified spatiotemporal variability in the occurrence and biomass of prey in response to an abrupt change in oceanography as a result of a marine heatwave event. Secondly, using multivariate techniques and machine learning, we investigated if differences in the foraging technique and prey of seabirds resulted in varying responses to changes in prey occurrence and the environment over a 2.5‐yr period. We found that the main variables correlated with seabird distribution were also important in structuring the occurrence and biomass of prey; sea‐surface temperature (SST), current speed, mixed‐layer depth, and bathymetry. Both zooplankton biomass and the occurrence of fish schools exhibited negative relationships with temperature, and temperature was subsequently an important variable in determining seabird distribution. We were able to establish correlations between the distribution of prey and the spatiotemporal distribution of albatross, little penguins and common‐diving petrels. We were unable to find a correlation between the distribution of prey and that of short‐tailed shearwaters and fairy prions. For high‐use coastal areas, the delineation of important foraging regions is essential to balance human use of an area with the needs of marine predators, particularly seabirds.

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Using a new framework of two-phase generalized additive models to incorporate prey abundance in spatial distribution models of juvenile slender lizardfish in Haizhou Bay, China

Cited by 20 publications

References 53 publications

Population status and distribution of whitespotted conger (Conger myriaster) in Yellow Sea: An important migratory species along coastal China with limited data

Population status and distribution of whitespotted conger (Conger myriaster) in Yellow Sea: An important migratory species along coastal China with limited data

Machine learning highlights the importance of primary and secondary production in determining habitat for marine fish and macroinvertebrates

Predicting the distribution of foraging seabirds during a period of heightened environmental variability

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