Spatially continuous data analysis and modelling

Saveliev, Anatoly A.; Mukharamova, S. S.; Chizhikova, N. A.; Budgey, R.; Zuur, Alain F.

doi:10.1007/978-0-387-45972-1_19

Cited by 15 publications

(11 citation statements)

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“…To identify spatial patterns of recruitment, mapped grids of each predictor variable were developed. Environmental variables were measured at each sampling event (n = 34,347) and these values were interpolated into mapped grids using ordinary kriging (Saveliev et al, 2007;Elith et al, 2008;Froeschke et al, 2010a). Suites of environmental conditions were developed for each month (May-December).…”

Section: Modeling Approachmentioning

confidence: 99%

Spatio-temporal predictive model based on environmental factors for juvenile spotted seatrout in Texas estuaries using boosted regression trees

Froeschke

2011

Fisheries Research

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Section: Modeling Approachmentioning

confidence: 99%

Spatio-temporal predictive model based on environmental factors for juvenile spotted seatrout in Texas estuaries using boosted regression trees

Froeschke

2011

Fisheries Research

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“…Habitat suitability models: Kriging is a spatial interpolation algorithm that was used to predict values at unsampled sites in the study area (Saveliev et al 2007). This method uses the variogram to express the spatial variation, and it minimizes the error of predicted values, which are estimated by spatial distribution of the predicted values.…”

Section: Boosted Regression Treesmentioning

confidence: 99%

“…In addition to identifying important environmental variables contributing to shark distribution patterns, we also wanted to generate spatially explicit predictions of catch probability at locations withheld during model training. We predicted the probability of capture to each site in the testing data set (n = 9878) using a form of logistic regression (Elith et al 2008) where the probability that a species occurs (y = 1), at a location with covariates X, P(y = 1 | X) using the logit: logit(P (y = 1 | X ) = f (X )).Habitat suitability models: Kriging is a spatial interpolation algorithm that was used to predict values at unsampled sites in the study area (Saveliev et al 2007). This method uses the variogram to express the spatial variation, and it minimizes the error of predicted values, which are estimated by spatial distribution of the predicted values.…”

mentioning

confidence: 99%

Environmental influences on the occurrence of coastal sharks in estuarine waters

Froeschke¹,

Stunz

Wildhaber³

2010

Mar. Ecol. Prog. Ser.

131

139

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Long-term fisheries independent gill net surveys conducted in Texas estuaries from 1975 to 2006 were used to develop spatially explicit estuarine habitat use models for 3 coastal shark species: bull shark Carcharhinus leucas, blacktip shark C. limbatus, and bonnethead shark Sphyrna tiburo. Relationships between environmental predictors and shark distribution were investigated using boosted regression trees (BRT). Bull shark was the most abundant species (n = 5800), followed by blacktip (n = 2094), and bonnethead sharks (n = 1793). Environmental conditions influenced distribution patterns of all species and relationships were nonlinear, multivariate, and interactive. Results showed very good model performance and suggested shark distribution is most closely linked to salinity, temperature, and proximity to tidal inlets. By interpolating the BRT models, maps of the probability of capture were produced using ordinary kriging. Results showed that the central region along the Texas coast contains the most important estuarine shark habitat. This area was characterized by warm temperatures, moderate salinities, and abundant tidal inlets. Bull sharks also extended into low salinity estuaries, while blacktip and bonnethead sharks were restricted to areas near tidal passes with moderate salinities. Juvenile sharks were frequently captured, suggesting the Texas coast may constitute important nursery areas for all 3 species. The development of these spatially explicit models allows for prioritization and conservation of areas in a region that has great potential for human disturbance and climate change impacts. These results provide new insight into the habitat requirements of coastal sharks in the northwestern Gulf of Mexico and practical information for managing this resource. KEY WORDS: Shark · Boosted regression trees · Essential fish habitat Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 407: [279][280][281][282][283][284][285][286][287][288][289][290][291][292] 2010 developing spatially explicit habitat maps for management purposes as animal abundance or productivity is directly linked to the amount of suitable areas available (Stoner 2003, Valavanis et al. 2008. Despite this recognition, delineation of essential habitat has been slow for many species in part because necessary data are often unavailable or analytical techniques have been unable to reliably identify critical habitat from available data. Moreover, predicting distributions of large, rare animals based on habitat characteristics can be difficult (Rooper & Martin 2009). Sampling requires adequate spatial and temporal coverage and must account for a large number of 'zero observations' in the assessment of species such as sharks.A suite of environmental variables has been hypothesized to influence elasmobranch distributions including temperature (Morrissey & Gruber 1993, Matern et al. 2000, Ortega et al. 2009), oxygen concentration (Parsons & Carlson 1998, Heithaus et al. 2009), salinity (Heupel ...

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“…Spatial grids.-Environmental variables were measured at each gill-net set (n = 24,756) and subsequently interpolated into raster grids using ordinary kriging (Saveliev et al 2007;Elith et al 2008; via the "autoKrige" function in the automap package in R (Hiemstra et al 2009). Environmental grids were developed for each month (April, May, June, September, October, and November) and year combination between 1980 and 2012 to permit spatially specific predictions during specific months and/or time periods.…”

Section: Froeschke and Froeschkementioning

confidence: 99%

Two‐Stage Boosted Regression Tree Model to Characterize Southern Flounder Distribution in Texas Estuaries at Varying Population Sizes

Froeschke

2016

Mar Coast Fish

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Linking trends in fish population abundance to environmental characteristics is often difficult because fish use a variety of habitats throughout their ontogeny and may exhibit large interannual fluctuations in abundance. We developed a two‐stage boosted regression tree model to investigate spatiotemporal patterns of Southern Flounder Paralichthys lethostigma abundance and distribution in Texas estuaries. We used a 36‐year fishery‐independent data set (1977–2012) to correlate distribution with environmental conditions and seasonal or long‐term changes in abundance. Adult Southern Flounder were sampled with gill nets using a random‐stratified design. Predictions of abundance were made to grids of environmental data to identify “hot spots” as well as seasonal or decadal shifts in distribution. Models were fit using cross validation, and variance was estimated using nonparametric bootstrapping. Depth, temperature, distance to a tidal inlet, and salinity were the primary environmental determinants of Southern Flounder distribution and abundance. Because distribution and response to environmental conditions can depend on population abundance, we also developed a standardized index of annual abundance using the same two‐stage boosted regression tree model. The index identifies a long‐term decline in abundance punctuated by recent recovery (possibly in response to management actions). Mapped results identify a coastwide decline in abundance between 1980–1984 and 2005–2009 but the magnitude varied substantially, suggesting disproportionate changes in abundance across the study area. Received April 10, 2015; accepted July 28, 2015

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Spatially continuous data analysis and modelling

Cited by 15 publications

References 0 publications

Spatio-temporal predictive model based on environmental factors for juvenile spotted seatrout in Texas estuaries using boosted regression trees

Spatio-temporal predictive model based on environmental factors for juvenile spotted seatrout in Texas estuaries using boosted regression trees

Environmental influences on the occurrence of coastal sharks in estuarine waters

Two‐Stage Boosted Regression Tree Model to Characterize Southern Flounder Distribution in Texas Estuaries at Varying Population Sizes

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