Spatial prediction of coral reef habitats: integrating ecology with spatial modeling and remote sensing

Garza-Pérez, Joaquín Rodrigo; Lehmann, Anthony; Arias-Gonzalez, J. Ernesto

doi:10.3354/meps269141

Cited by 56 publications

(28 citation statements)

References 45 publications

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“…2) (Overton et al 2002;Garza-Pérez et al 2004). We have performed the spatial diversity prediction by using fish abundance (number of individuals), SR (species richness or the number of species) and the Shannon-Weaver diversity index as a function of a variety of environmental variables, which were modeled using GRASP v.2.5 (Generalized regression analysis and spatial prediction) (Lehmann et al 2002a).…”

Section: Spatial Predictionmentioning

confidence: 99%

“…Recent studies on coral reefs addressing spatially explicit predictions encompass a wide range of applications: covers of coral reef benthic components and diversity (Garza-Pérez et al 2004) using generalized additive models (GAMs); fish SR using classification and regression trees (CART) (Pittman et al 2007); juvenile coral reef fishes distribution using generalized linear models (GLMs) (Mellin et al 2007); and fish community structure indicators using different models (Knudby et al 2010;Mellin et al 2010;Pittman and Brown 2011). All these studies have used different approaches to predict benthic and fish coral reef communities successfully.…”

Section: Introductionmentioning

confidence: 98%

“…We chose to test this tool as a coral reef fish biodiversity predictor as it has been proved to be a very useful tool in predicting biodiversity distribution in terrestrial ecosystems and in the prediction of benthic coral reef communities' cover (Ferrier et al 2002;Lehmann et al 2002b;Garza-Pérez et al 2004;Moser et al 2005;Maggini et al 2006). GRASP uses GAM's models to relate response variables, such as biodiversity, with environmental variables.…”

Section: Introductionmentioning

confidence: 99%

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Predicting spatially explicit coral reef fish abundance, richness and Shannon–Weaver index from habitat characteristics

Arias‐González¹,

Acosta‐González²,

Membrillo³

et al. 2011

Biodivers Conserv

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The assessment of biodiversity in coral reefs requires the application of geographic information systems (GIS), remote sensing and analytical tools in order to make cost-effective spatially explicit predictions of biodiversity over large geographic areas.Here we present a spatially explicit prediction for coral reef fish diversity index, as well as habitat classification according to reef fish diversity index values in Chinchorro Bank Biosphere Reserve, one of the most important plain/atoll type reef systems in the Caribbean. We have used extensive ecological data on depth, fish and habitat characteristics to perform such prediction. Fish species assemblages and different biotic variables of benthic organisms were characterized using visual censuses and video-transects, respectively at 119 sampling stations. The information was integrated in a GIS, along with satellite imagery (LANSDAT 7 ETM?) and a digital bathymetric model. From the recorded data and a hierarchical classification procedure, we obtained nine different classes of habitats. We used a generalized regression analysis and spatial prediction methodology to create predictive maps (GIS layers) of the different reef benthic components, and a second modeling run produced predictive maps of coral reef fish diversity index. Predictive accuracy of the diversity index map presented a good correlation coefficient (r = 0.87), with maximum diversity index values en reefscapes composed of aggregation of coral colonies with seagrass beds. The implementation of our application was successful for the prediction of fish diversity hot spots and surrogate habitats.

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Section: Spatial Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Predicting spatially explicit coral reef fish abundance, richness and Shannon–Weaver index from habitat characteristics

Arias‐González¹,

Acosta‐González²,

Membrillo³

et al. 2011

Biodivers Conserv

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“…In situ images are typically used to define a typology of habitats relevant for understanding species distributions at scales ,100 m (Dumas et al 2009), whereas remotely sensed images are used to map these habitats and associated species at scales .100 m (e.g., Mattio et al 2008). These habitat maps are increasingly used as spatially explicit layers in habitat suitability models, whereby biodiversity metrics (e.g., species richness, abundance, functional groups) can be mapped indirectly (Garza-Pe´rez et al 2004, Mellin et al 2007). However, these methods are time-consuming, often requiring some form of field validation, are scale-specific, and are rarely transferrable across different study areas.…”

Section: Introductionmentioning

confidence: 99%

Multi‐scale marine biodiversity patterns inferred efficiently from habitat image processing

Mellin¹,

Parrott²,

Andréfouët³

et al. 2012

Ecological Applications

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Abstract. Cost-effective proxies of biodiversity and species abundance, applicable across a range of spatial scales, are needed for setting conservation priorities and planning action. We outline a rapid, efficient, and low-cost measure of spectral signal from digital habitat images that, being an effective proxy for habitat complexity, correlates with species diversity and requires little image processing or interpretation. We validated this method for coral reefs of the Great Barrier Reef (GBR), Australia, across a range of spatial scales (1 m to 10 km), using digital photographs of benthic communities at the transect scale and high-resolution Landsat satellite images at the reef scale. We calculated an index of image-derived spatial heterogeneity, the mean information gain (MIG), for each scale and related it to univariate (species richness and total abundance summed across species) and multivariate (species abundance matrix) measures of fish community structure, using two techniques that account for the hierarchical structure of the data: hierarchical (mixed-effect) linear models and distance-based partial redundancy analysis. Over the length and breadth of the GBR, MIG alone explained up to 29% of deviance in fish species richness, 33% in total fish abundance, and 25% in fish community structure at multiple scales, thus demonstrating the possibility of easily and rapidly exploiting spatial information contained in digital images to complement existing methods for inferring diversity and abundance patterns among fish communities. Thus, the spectral signal of unprocessed remotely sensed images provides an efficient and lowcost way to optimize the design of surveys used in conservation planning. In data-sparse situations, this simple approach also offers a viable method for rapid assessment of potential local biodiversity, particularly where there is little local capacity in terms of skills or resources for mounting in-depth biodiversity surveys.

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“…For example, Kelly et al (2001) predicted seagrass occurrences based on a relative exposure index and water depth with a logistic regression model. Bathymetry data and spectral bands from remote imagery were successfully used to predict the presence of coral communities in Mexico (Garza-Perez et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Environmental factors that influence the distribution of coral reef fishes: modeling occurrence data for broad-scale conservation and management

Beger¹,

Possingham²

2008

Mar. Ecol. Prog. Ser.

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To manage coral reef species, it is important to identify the factors that determine their distribution inexpensively. We identified the remotely measured environmental factors that are most influential in determining the distributions of coral reef fish species on a regional scale. Logistic regression models for 227 fish species related presence/absence data to 4 remotely determined environmental predictor variables: depth, presence of a land -sea interface, exposure, and the distance to the nearest estuary. We compared modeled Akaike information criterion (AIC) values with AIC values of randomly distributed species with different numbers of occurrences and levels of habitat specificity to evaluate model significance. Efficient species distribution models were identified for 118 predominantly habitat-specific fishes of the 227 species for which we had data. All 4 predictor variables significantly influenced the distributions of at least some fish species. Depth was the most frequently efficient variable for single variable models. For combinations of 2 predictor variables, depth and exposure, as well as depth and distance from the nearest estuary, were the prevalent predictors of fish distributions. Several fish species responded to the combination of variables distance from an estuary and presence of the terrestrial-marine interface, indicating that these species depend on intact coastal reef habitat, which is in decline near the main sediment-laden rivers. Statistically significant models were predominantly developed for habitat-specific species. These habitatspecific species are of greater conservation concern than widespread species because threats affect them more severely if the threats are selectively affecting their habitat. For this reason, species distribution modeling using remotely determined environmental data may be an efficient method to build models for habitatspecific species and inform marine reserve design.

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Spatial prediction of coral reef habitats: integrating ecology with spatial modeling and remote sensing

Cited by 56 publications

References 45 publications

Predicting spatially explicit coral reef fish abundance, richness and Shannon–Weaver index from habitat characteristics

Predicting spatially explicit coral reef fish abundance, richness and Shannon–Weaver index from habitat characteristics

Multi‐scale marine biodiversity patterns inferred efficiently from habitat image processing

Environmental factors that influence the distribution of coral reef fishes: modeling occurrence data for broad-scale conservation and management

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