Ensemble habitat suitability modeling of stomatopods with Oratosquilla oratoria as an example
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Abstract: Stomatopods are better known as mantis shrimp with considerable ecological importance in wide coastal waters globally. Some stomatopod species are exploited commercially, including Oratosquilla oratoria in the Northwest Pacific. Yet, few studies have published to promote accurate habitat identification of stomatopods, obstructing scientific management and conservation of these valuable organisms. This study provides an ensemble modeling framework for habitat suitability modeling of stomatopods, utilizing the O. oratoria stock in the Bohai Sea as an example. Two modeling techniques (i.e., generalized additive model (GAM) and geographical weighted regression (GWR)) were applied to select environmental predictors (especially the selection between two types of sediment metrics) that better characterize O. oratoria distribution and build separate habitat suitability models (HSM). The performance of the individual HSMs were compared on interpolation accuracy and transferability. Then, they were integrated to check whether the ensemble model outperforms either individual model, according to fishers’ knowledge and scientific survey data. As a result, grain-size metrics of sediment outperformed sediment content metrics in modeling O. oratoria habitat, possibly because grain-size metrics not only reflect the effect of substrates on burrow development, but also link to sediment heat capacity which influences individual thermoregulation. Moreover, the GWR-based HSM outperformed the GAM-based HSM in interpolation accuracy, while the latter one displayed better transferability. On balance, the ensemble HSM appeared to improve the predictive performance overall, as it could avoid dependence on a single model type and successfully identified fisher-recognized and survey-indicated suitable habitats in either sparsely sampled or well investigated areas.
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Key words:
- habitat suitability /
- stomatopod /
- coastal fisheries /
- predictor selection /
- ensemble model
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Figure 1. The distributions of successfully sampled stations in April (circles), May (triangles) and June (plus signs) in the Bohai Sea with bathymetric information from the monthly bottom trawl surveys conducted by the Yellow Sea Fisheries Research Institute in 2017 (a); frequency distribution of log-transformed Oratosquilla oratoria densities in these stations, except six stations without catch of this species (b). D: density, g/h.
Figure 2. View of generalized additive model (GAM) (a) and geographical weighted regression (GWR) model (b) selection with eight environmental predictors and Akaike Information Criterion corrected (AICc) (c) values of all alternative models. AICc of the optimal models was shaded in red. BT: bottom temperature; BS: bottom salinity; D: density.
Figure 3. Spline smooth regression curves of five environmental predictors included in the optimal generalized additive model showing their respective effects on Oratosquilla oratoria density. BT: bottom temperature; BS: bottom salinity.
Figure 4. Local coefficient estimates derived from geographically weighted regressions of log-scaled Oratosquilla oratoria density to separate environmental predictors included in the optimal geographical weighted regression model. BT: bottom temperature.
Figure 5. Mapping of generalized additive model (GAM)-based habitat suitability index (HSI) (a, c, e) and geographical weighted regression (GWR)-based HSIs (b, d, f) for Oratosquilla oratoria in April, May and June 2017. The density (g/h) distributions of O. oratoria in corresponding months of 2017 were plotted as circles over the HSI maps. Polygons in panels c and d show areas for three place-based O. oratoria fisheries in May 2021.
Figure 6. Scatter plots and linear relationships of predicted habitat suitability index (HSI) from generalized additive model (GAM)- and geographical weighted regression (GWR)-based habitat suitability models against log-transformed Oratosquilla oratoria densities (lg D; D, unit: g/h) at observed stations in April (blue circles), May (cyan triangles) and June (pink plus signs) 2017.
Figure 7. Mapping of habitat suitability index (HSI) from the ensemble habitat suitability models for Oratosquilla oratoria in April (a), May (b) and June (c) 2017. The density (g/h) distributions of O. oratoria in corresponding months of 2017 were plotted as circles over respective HSI maps. Polygons in panel b show areas for three place-based O. oratoria fisheries in May 2021.
Table 1. Sediment grain sizes associated with Wentworth classes and Krumbein phi (Φ) ranges
Grain size/mm Grain size/μm Wentworth class Phi (Φ) range 1 to 2 1000 to 2000 very coarse sand Φ=–1 to Φ=0 1/2 to 1 500 to 1000 coarse sand Φ=0 to Φ=1 1/4 to 1/2 250 to 500 medium sand Φ=1 to Φ=2 1/8 to 1/4 125 to 250 fine sand Φ=2 to Φ=3 1/16 to 1/8 62.5 to 125 very fine sand Φ=3 to Φ=4 1/32 to 1/16 31.25 to 62.5 coarse silt Φ=4 to Φ=5 1/64 to 1/32 15.625 to 31.25 medium silt Φ=5 to Φ=6 1/128 to 1/64 7.813 to 15.625 fine silt Φ=6 to Φ=7 1/256 to 1/128 3.906 to 7.813 very fine silt Φ=7 to Φ=8 < 1/256 < 3.906 clay Φ>8 
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Table 2. Integer values of sediment metrics in this study corresponding to the intervals of sediment variables from Yuan et al. (2020)
Ord_Psand Sand content Ord_Pclay Clay content Ord_Mz Mean size (Φ) Ord_Sk Skewness Ord_Kt Kurtosis 1 0 to 10% 1 0 to 5% 1 Φ<3 1 –2.5 to –1.5 1 0.5 to 1.5 2 10% to 20% 2 5% to 10% 2 Φ=3 to Φ=4 2 –1.5 to –0.33 2 1.5 to 2.5 3 20% to 30% 3 10% to 15% 3 Φ=4 to Φ=5 3 –0.33 to 0.33 3 2.5 to 2.75 4 30% to 40% 4 15% to 20% 4 Φ=5 to Φ=6 4 0.33 to 1.5 4 2.75 to 3 5 40% to 50% 5 20% to 25% 5 Φ=6 to Φ=7 5 1.5 to 2.2 5 3 to 3.25 6 50% to 60% 6 25% to 30% 6 Φ>7 6 2.2 to 3.5 6 3.25 to 3.5 7 60% to 70% 7 30% to 35% – – 7 > 3.5 7 3.5 to 4.5 8 70% to 80% 8 35% to 40% – – – – 8 4.5 to 5.5 − − − − − − − − 9 >5.5
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