Techniques for cetacean-habitat modeling

By Jessica V. Redfern, M. Ferguson, Elizabeth A. Becker, K. Hyrenbach, C. P. Good, Jay Barlow, K. Kaschner, M. Baumgartner, Karin A. Forney, L. Ballance and 9 more. 2006

453 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/5e4b10e3228df8ac10d9877dcb081cbfca874d70

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Citations in the corpus

Spatial association between hotspots of baleen whales and demographic patterns of Antarctic krill Euphausia superba suggests size-dependent predation (2010, methodology): “…patterns and spatial distribution of whale populations in the Southern Ocean is necessary, as historically predictable feeding areas may be altered by climate change (de la Mare 1997, Tynan 1998, Kaschner et al. 2006, Redfern et al. 2006), potentially impacting recovery of some populations.”
Identifying essential fish habitat for small pelagic species in Spanish Mediterranean waters (2008, background): “A wide range of techniques is now available to model habitat requirements and these have been extensively reviewed (e.g. Guisan & Zimmermann, 2000; see also Redfern et al., 2006 for a particular review on marine species).”
The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling (2012, methodology): “This kind of approach has been increasingly used and examples include predictions of species interacting as predator and prey (e.g. Redfern et al., 2006), animal and food plants (Preston et al., 2008; Schweiger et al., 2011, 2008), in resource competition (e.g. Meier et al., 2010b; Pellissier et…”
Fine-scale prey aggregations and foraging ecology of humpback whales Megaptera novaeangliae (2009, methodology): “To examine thresholds in predator behavior, a classification and regression tree (CART; Redfern et al. 2006) analysis was conducted with whale behavior (surface feeding/other) as a binomial response variable with 24 environmental and prey variables as predictors.”
Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities (2011, background): “…of prey on the distribution of consumers relative to the effect of the environment is rarely examined, but there are a few more examples in marine (Logerwell & Hargreaves, 1996; Redfern et al., 2006; Torres et al., 2008) than terrestrial systems (Fernandez et al., 2003; Guisan & Thuiller, 2005).”
Oceanographic influences on seabirds and cetaceans of the eastern tropical Pacific: A review (2006, background): “Analytical tools fall into two basic categories (reviewed by Redfern et al., 2006): descriptive techniques (e.g. overlays of species data on maps of oceanographic measures, correlation analysis, goodness of fit metrics, analysis of variance, and ordination) and modeling techniques (e.g.…”
Seasonal habitat-based density models for a marine top predator, the harbor porpoise, in a dynamic environment (2016, background): “…at finer spatial and temporal scales than conventional, June 2016 v Volume 7(6) v Article e013673 v www.esajournals.org design- based line- transect analyses (see reviews in Redfern et al. 2006, Gregr et al. 2013); therefore, such model- based densities can improve fine- scale management needs.”
Using marine mammal habitat modelling to identify priority conservation zones within a marine protected area (2009, background): “Distribution patterns often exhibit a hierarchical structure, and these models can therefore provide a powerful tool for assessing areas of high relative density within MPAs, and for determining what factors influence distribution (Redfern et al. 2006).”
Whale distribution in relation to prey abundance and oceanographic processes in shelf waters of the Western Antarctic Peninsula (2006, methodology): “…tool because, unlike linear models, they do not assume an a priori relationship between the response variable and predictor variables; rather, the data are divided into several groups where each has a different predicted value of the response variable (Guisan & Zimmerman 2000, Redfern et al. 2006).”
Comparing California Current cetacean-habitat models developed using in situ and remotely sensed sea surface temperature data (2010, background): “164 Becker et al.: California Current cetacean–habitat model comparison Model development: GLMs and GAMs are commonly used to relate environmental data to characteristics of a species, such as distribution or abundance (see Redfern et al. 2006).”
Summer distribution patterns of southern resident killer whales Orcinus orca: core areas and spatial segregation of social groups (2007, background): “Describing distribution patterns of cetaceans becomes more complicated when considering the social organization and behavior of these species (Redfern et al. 2006), and within a given species, space use can vary for distinct populations and population segments (e.g. Whitehead 1997).”
Summer spatial distribution of cetaceans in the Strait of Gibraltar in relation to the oceanographic context (2008, background): “Therefore, sampling effort is typically heterogeneous and needs to be carefully accounted for (Redfern et al. 2006).”
Evidence of resource partitioning between humpback and minke whales around the western Antarctic Peninsula (2009, background): “…177 because, unlike linear models, they do not assume a priori relationships between 178 response and predictor variables; rather the data are divided into several groups where 179 each has a different predicted value of the response variable (Guisan and Zimmerman 180 2000, Redfern et al. 2006).”
Combining vessel-based surveys and tracking data to identify key marine areas for seabirds (2009, unknown): “However, presence/absence data rely on the need to obtain standardized absence data and the reliance on arbitrary criteria of the minimum amount of survey effort required to ascertain the absence of the species of interest in the sampling unit being considered (Redfern et al. 2006).”