Predictive modelling of mesophotic habitats in the north‐western Gulf of Mexico

Sterne, Travis K.; Retchless, David; Allee, Rebecca J.; Highfield, Wesley E.

doi:10.1002/aqc.3281

Cited by 9 publications

(5 citation statements)

References 37 publications

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“…The highest gridding resolution of bathymetry data determined the finest spatial scale (2 m). The appropriate broad scale was determined using Dragut's Estimation of Scale Parameter (ESP) Tool (Drǎguţ et al 2010), where a decline in Rate of Change of Local Variance (ROC-LV) curves was observed at a resolution of 25 m. Derivatives were also calculated at 10 m resolution to account for intermediate-scale effects and following resolutions used in other mesophotic reef studies (Costa et al 2015;Sterne et al 2020). Although variables at multiple scales exhibited collinearity, these were retained to account for possible multiscale effects of predictors on assemblage occurrence and distribution.…”

Section: Environmental Predictorsmentioning

confidence: 99%

“…Assemblage 2 contained both zooxanthellate and azooxanthellate species and is therefore in part associated with depths where sufficient light penetrates to enable photosynthesis. Assemblage 2 was also associated with slope and curvature which mediate hydrodynamic properties of water flow past the reef system, influencing food availability for heterotrophic suspension-feeding organisms (Locker et al 2010;Bridge et al 2011b;Sterne et al 2020). Increased slope values may also shield taxa characterized by flattened morphologies found in assemblage 2 from heavy sedimentation (Ohlhorst and Liddell 1988;Kahng et al 2010).…”

Section: Drivers and Distribution Of Mesophotic Assemblagesmentioning

confidence: 99%

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Geomorphological drivers of deeper reef habitats around Seychelles

et al. 2022

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Mesophotic (30–150 m) and rariphotic (150–300 m) deeper reef habitats are important from an ecological and conservation perspective, yet remain understudied. Key knowledge gaps exist on the environmental patterns and processes that drive and shape their geographical distributions. Understanding these is particularly important for regions as the Western Indian Ocean, where deeper reefs are poorly known but support food security and host economically important species. Spatial predictive models of assemblage occurrences, using terrain variables as predictors, offer a solution to address knowledge gaps around deeper reef distributions. We identified relationships between seafloor geomorphology, quantified at multiple scales, and sessile benthic assemblages in four atoll seascapes in Seychelles using terrain models derived from high-resolution multibeam sonar and underwater video surveys. Using random forests and boosted regression trees, we demonstrated that terrain derivatives extracted over multiple scales perform as reliable predictors of deeper reef assemblages. The most influential environmental predictors were depth, distance to shore, topographic complexity, slope and curvature and substrate characteristics. The relative importance of predictors was explained by assemblage functional characteristics. Assemblage–environment relationships were used to produce probability distribution maps that showed similar distributional patterns for identified assemblages across locations, with high occurrence probabilities linked to complex geomorphological structures. Our results help contribute to a consistent baseline understanding of the relationship between seascape structure and mesophotic reef ecosystems in this area. Complex geomorphological structures, including terraces and paleoshorelines, supported high densities of mesophotic assemblages and could be considered priority habitats for management.

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Section: Environmental Predictorsmentioning

confidence: 99%

Section: Drivers and Distribution Of Mesophotic Assemblagesmentioning

confidence: 99%

Geomorphological drivers of deeper reef habitats around Seychelles

et al. 2022

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“…Notably, MaxEnt, in accordance with the principle of maximum entropy [19,22,26], is frequently regarded as outperforming other species distribution models (SDMs) due to its strong toleration and precise forecasting in many model intercomparisons [27][28][29]. Researchers worldwide in the last decade have achieved significant success in applying species distribution models to issues such as protecting the diversity of rare animals and plants [30][31][32][33], estimating the dangers of invasive species [34][35][36], protecting marine ecosystem [37,38], predicting disaster distribution [39], and disease propagation [40,41], employing the MaxEnt model.…”

Section: Introductionmentioning

confidence: 99%

Modeling Climate Change Indicates Potential Shifts in the Global Distribution of Orchardgrass

Yan

Zhao

et al. 2023

Agronomy

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Orchardgrass (Dactylis glomerata L.) is highly tolerant of shade, cold, and overwintering, making it an ideal species for grassland ecological restoration and livestock production. However, the genetic diversity of orchardgrass may be threatened by climate change. Using a Maximum Entropy (MaxEnt) model with the BCC-CSM2-MR global climate database and the Harmonized World Soil Database, we projected the current and future distribution of orchardgrass suitable areas globally. The predicted ecological thresholds for vital environmental factors were determined to be a temperature seasonality range of 411.50–1034.37 °C, a mean diurnal range of −0.88–10.69 °C, a maximum temperature of the warmest month of 22.21–35.45 °C, and precipitation of the coldest quarter of 116.56–825.40 mm. A range of AUC values from 0.914 to 0.922, indicating the accuracy of the prediction model. Our results indicate that the total area of current suitable habitats for orchardgrass was estimated to be 2133.01 × 104 km2, it is dispersed unevenly over six continents. Additionally, the suitable areas of habitats increased in higher latitudes while decreasing in lower latitudes as greenhouse gas emissions increased. Therefore, efforts should be made to save places in the southern hemisphere that are in danger of becoming unsuitable, with the possibility of using northern America, China, and Europe in the future for conservation and extensive farming.

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“…Many model intercomparison studies have reported that the MaxEnt model, which is based on the principle of maximum entropy 23 , 26 , 30 , typically outperforms other species distribution models (SDMs) in terms of high tolerance and high predictive accuracy 31 – 33 . Over the past 10 years, worldwide research teams have achieved excellent results in the study of rare animal and plant diversity protection 34 – 38 , invasive species risk prediction 39 – 41 , marine ecosystem protection 42 , 43 , disaster distribution prediction 44 , and disease propagation 45 , 46 using the MaxEnt model. Zhang et al 47 found that the area of suitable range of Cinnamomum camphora (L.) Presl will increase and continue to move to the northwest of China under future climate change scenarios.…”

Section: Introductionmentioning

confidence: 99%

Global distribution of soapberries (Sapindus L.) habitats under current and future climate scenarios

Liu

Wang

Sun

et al. 2021

Sci Rep

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Sapindus (Sapindus L.) is a widely distributed economically important tree genus that provides biodiesel, biomedical and biochemical products. However, with climate change, deforestation, and economic development, the diversity of Sapindus germplasms may face the risk of destruction. Therefore, utilising historical environmental data and future climate projections from the BCC-CSM2-MR global climate database, we simulated the current and future global distributions of suitable habitats for Sapindus using a Maximum Entropy (MaxEnt) model. The estimated ecological thresholds for critical environmental factors were: a minimum temperature of 0–20 °C in the coldest month, soil moisture levels of 40–140 mm, a mean temperature of 2–25 °C in the driest quarter, a mean temperature of 19–28 °C in the wettest quarter, and a soil pH of 5.6–7.6. The total suitable habitat area was 6059.97 × 104 km2, which was unevenly distributed across six continents. As greenhouse gas emissions increased over time, the area of suitable habitats contracted in lower latitudes and expanded in higher latitudes. Consequently, surveys and conservation should be prioritised in southern hemisphere areas which are in danger of becoming unsuitable. In contrast, other areas in northern and central America, China, and India can be used for conservation and large-scale cultivation in the future.

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Predictive modelling of mesophotic habitats in the north‐western Gulf of Mexico

Cited by 9 publications

References 37 publications

Geomorphological drivers of deeper reef habitats around Seychelles

Geomorphological drivers of deeper reef habitats around Seychelles

Modeling Climate Change Indicates Potential Shifts in the Global Distribution of Orchardgrass

Global distribution of soapberries (Sapindus L.) habitats under current and future climate scenarios

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