Using Lidar Bathymetry and Boosted Regression Trees to Predict the Diversity and Abundance of Fish and Corals

By SJ Pittman, B. Costa, Timothy Battista. 2009

206 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/599b5091c79fb7a729ee8317f96af43543832f3e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. Gretchen G. Moisen
2. Elizabeth A. Freeman
3. Euan Harvey
4. C. Boström
5. V. Versace
6. G. Quinn
7. Jacquomo Monk
8. C. Simenstad

In Ecology

1. K. L. Nash
2. SJ Pittman
3. Rod M. Connolly
4. C. Simenstad
5. R. Kneib
6. C. Boström
7. Nicholas A. J. Graham
8. Euan Harvey

In Environmental Engineering

1. Elizabeth A. Freeman
2. Gretchen G. Moisen
3. B. Wilson
4. Nicholas A. J. Graham
5. SJ Pittman
6. Michael P. Austin
7. Wilfried Thuiller
8. B. Schr

Citations in the corpus

• The importance of structural complexity in coral reef ecosystems (2012, background): “colony level to remote techniques such as side-scan sonar which can provide cross-reef assessments of complexity (e.g. Holbrook et al. 2002; Pittman et al. 2009).”
• Primacy of seascape connectivity effects in structuring coral reef fish assemblages (2012, background): “They can fit non- linear relationships and are robust to colinearity among predictors, their power is not reduced by the presence of irrelevant variables, and they can model interaction and threshold effects (Pittman et al. 2009).”
• Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance (2016, background): “Pittman et al. (2009) found that SGB worked well to predict the diversity and abundance of fish and corals from underwater Lidar bathymetry with parameters left at their default values.”
• Linking temperate demersal fish species to habitat: scales, patterns and future directions (2013, background): “They are, however, not good predictors of habitat suitability for new or unsampled sites (Maravelias et al. 2003; Pittman et al. 2009; Knudby et al. 2010).”
• Seascape ecology of coastal biogenic habitats: advances, gaps, and challenges (2011, background): “In addition, an important third model has recently emerged that represents structural heterogeneity as a continuous gradient without discrete patch boundaries, such as with a digital terrain model of bathymetry or sea surface temperature (McGarigal & Cushman 2002, Pittman et al. 2009).”
• Habitat suitability for marine fishes using presence-only modelling and multibeam sonar (2010, background): “…studies have predominantly focused on how variations in predictor variables 158 Monk et al.: Predicting marine fish habitat suitability (e.g. seafloor characteristics such as bathymetry or rugosity) affect the ability of the models to predict species distributions (e.g. Pittman et al. 2009).”