Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean

Pittman, Simon J.; Christensen, John D.; Caldow, Chris; Menza, Charles W.; Monaco, Mark E.

doi:10.1016/j.ecolmodel.2006.12.017

Cited by 151 publications

(159 citation statements)

References 46 publications

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“…Habitat richness was also of low importance, regardless of model type and spatial scale. Although this corresponds to findings from other studies (e.g., [16,17]), the question remains whether other measures of the spatial composition of habitats, e.g., taking into account functional differences or complementarities between habitat types [63], would contribute more to predictions of the fish community variables. The success of landscape composition metrics in terrestrial spatial ecology [64] suggests that this is an important avenue for future research.…”

Section: Discussionmentioning

confidence: 72%

“…In the random forest models, the high importance of geomorphic zone for predictions of all response variables is interesting because this predictor has been investigated by very few studies [13], including several that have explored the use of a large variety of metrics for quantifying the seascape (e.g., [16][17][18]32,61]). The importance of geomorphic zone in our study may be a function of the spatial extent of our study area, a function of the high accuracy with which this variable was mapped, a function of its collinearity with another predictor, and/or a true description of the importance of reef geomorphology for structuring the spatial distribution of the three response variables in our study area.…”

Section: Discussionmentioning

confidence: 99%

“…For example, the positive effect of structural complexity on reef fish species richness and abundance has been demonstrated experimentally [7], and areas of high structural complexity have been shown to contain more species [8] and greater total biomass [9] compared to areas with less structure. Other factors have shown similar correlations with one or more aspects of reef fish communities, such as water depth [10], disturbance history [11], coral cover [12], location on the reef [9] and shelf [13], presence of seagrass beds and mangroves [14,15], as well as benthic composition and patch structure [16][17][18]. Such environmental variables, and the patch mosaic they create, interact with and influence the local fish communities at a range of spatial scales.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Fish Community Variables by Integrating Field and Satellite Data, Object-Based Image Analysis and Modeling in a Traditional Fijian Fisheries Management Area

et al. 2011

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Abstract:The use of marine spatial planning for zoning multi-use areas is growing in both developed and developing countries. Comprehensive maps of marine resources, including those important for local fisheries management and biodiversity conservation, provide a crucial foundation of information for the planning process. Using a combination of field and high spatial resolution satellite data, we use an empirical procedure to create a bathymetric map (RMSE 1.76 m) and object-based image analysis to produce accurate maps of geomorphic and benthic coral reef classes (Kappa values of 0.80 and 0.63; 9 and 33 classes, respectively) covering a large (>260 km 2 ) traditional fisheries management area in Fiji. From these maps, we derive per-pixel information on habitat richness, structural complexity, coral cover and the distance from land, and use these variables as input in models to predict fish species richness, diversity and biomass. We show that random forest models outperform five other model types, and that all three fish community variables can be satisfactorily predicted from the high spatial resolution satellite data. We also show geomorphic zone to be the most important predictor on average, with secondary contributions from a range of other variables including benthic class, depth, distance from land, and live coral cover mapped at coarse spatial scales, suggesting that data with lower spatial resolution and lower cost may be sufficient for spatial predictions of the three fish community variables. OPEN ACCESSRemote Sens. 2011, 3 461

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Section: Discussionmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping Fish Community Variables by Integrating Field and Satellite Data, Object-Based Image Analysis and Modeling in a Traditional Fijian Fisheries Management Area

et al. 2011

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“…Using habitat based ecological models selection and design of conservation areas or reevaluation of existing areas can be made (Love et al, 2006;Pittman, Christensen, Caldow, Menza, & Monaco, 2007). Although some of these models are not able to represent habitat needs of all species inhabiting a specific area, they can be developed for ecologically and/or economically valuable species (Lindeman, Pugliese, Waugh, & Ault, 2000).…”

Section: Resultsmentioning

confidence: 99%

Untitled

Rezaei¹,

Şengül²

2019

Turk. J. Fish. Aquat. Sci.

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Generalized Additive Models (GAMs) are widely used statistical models for species distribution in habitat and environmental management modeling as they enable incorporation of non-linearity. The objective of this study is to develop relationships between habitat variables, fish absence/presence (logistic GAM) and abundance (Poisson GAM) for the S. rizeensis using onsite observations in Solaklı River. Logistic GAMs correctly predicted absence/presence of adult and juvenile S. rizeensis at 79.3% and 74% of sampling areas respectively. Response curves of logistic GAMs results show differences in probability of finding the adult and juvenile fish. Adult S. rizeensis were mostly found in higher velocities (0.5-0.8 m/s) than in juvenile fish (≈ 0 m/s). Also, the highest number of adult fish were recorded in deeper habitats (≈ 0.4 m) rather than in juvenile fish (≈ 0 m). Curves show adult fish are more independent to the presence of cover compared to juvenile fish. Velocity shelter and bedrock formation were the most common cover types. Chi-square test results of predicted values showed that developed Poisson GAMs of adult and juvenile S. rizeensis could not accurately represent fish abundance. The results show that while logistic GAM is applicable, Poisson GAM model is not applicable for the area.

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“…It is very useful for predicting fish distributions in unsurveyed areas. Multiple regressions provide the most accurate predictions for high species richness class (Pittman et al 2007). Zainuddin (2007) showed the ability of multiple regression analysis to predict the abundance of R. kanagurta in Bantaeng waters of South Sulawesi Indonesia.…”

Section: Statistical Multiple Regression Modelmentioning

confidence: 99%

Determination of Potential Fishing Grounds of Rastrelliger kanagurta Using Satellite Remote Sensing and GIS Technique

Nurdin

Mustapha²,

Lihan³

et al. 2015

JSM

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Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean

Cited by 151 publications

References 46 publications

Mapping Fish Community Variables by Integrating Field and Satellite Data, Object-Based Image Analysis and Modeling in a Traditional Fijian Fisheries Management Area

Mapping Fish Community Variables by Integrating Field and Satellite Data, Object-Based Image Analysis and Modeling in a Traditional Fijian Fisheries Management Area

Untitled

Determination of Potential Fishing Grounds of Rastrelliger kanagurta Using Satellite Remote Sensing and GIS Technique

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