Hierarchical Species Distribution Models

By T. Hefley, Mevin B. Hooten. 2016

88 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/453dc64e121e46381d614d87fabcbbeb17ed45f3

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. Matt D. White

In Ecology

1. R. Dorazio
2. Matt D. White
3. Marc Kéry
4. J. Andrew Royle
5. D. MacKenzie

In Nature and Landscape Conservation

1. R. Dorazio
2. Matt D. White
3. Marc Kéry
4. J. Andrew Royle
5. D. MacKenzie

Citations in the corpus

• A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants (2020, unknown): “…2006), random forests ( rf ) (Evans et al., 2011; Svetnik et al., 2003), boosted regression trees ( brt ) (De'ath, 2002), and other machine learning methods such as artificial neural networks ( ANN ) (Zurada, 1992) or Bayesian heirarchical modelling (Hefley and Hooten, 2016; Latimer et al., 2006).”
• Integrated species distribution models: combining presence‐background data and site‐occupancy data with imperfect detection (2017, methodology): “In some contexts, SDMs may be used to study changes through time (Elith & Leathwick 2009; Hefley & Hooten 2016).”