Modelling species diversity through species level hierarchical modelling
By Alan E. Gelfand, Alexandra M. Schmidt, Shanshan Wu, John A. Silander, Andrew M. Latimer, A. Rebelo. 2005
158 citations recorded by the source corpus.
Subfields: Ecological Modeling, Economics and Econometrics, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/045cae1377c57679469b2c89310d893fa8e47456
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
- G. Guillera‐Arroita
- Antoine Guisan
- Marc Kéry
- Frank M. Schurr
- Loïc Pellissier
- J. Cabral
- J. Pottier
- C. Randin
In Economics and Econometrics
- G. Guillera‐Arroita
- Marc Kéry
- Antoine Guisan
- N. LeRoy Poff
- Alan E. Gelfand
- Beth Gardner
- Frank M. Schurr
- Guy F. Midgley
In Nature and Landscape Conservation
- G. Guillera‐Arroita
- Antoine Guisan
- Marc Kéry
- Frank M. Schurr
- Loïc Pellissier
- J. Cabral
- Guy F. Midgley
- J. Pottier
Citations in the corpus
- Where will conflicts between alien and rare species occur after climate and land-use change? A test with a novel combined modelling approach (2011, background): “Multi-scale approaches to SDMs, integrating predictors acting at different scales, may capture dimensions of species distributions that are ignored by approaches conducted at a single scale (Pearson et al. 2004; Gelfand et al. 2005).”
- Joint Species Distribution Modeling: Dimension Reduction Using Dirichlet Processes (2017, methodology): “Prediction of species over space can be accommodated using a spatially explicit specification (Gelfand et al., 2005, 2006; Latimer et al., 2006).”
- Predicting species distributions from checklist data using site‐occupancy models (2010, background): “Site-occupancy models also form the basis for the recently developed models for inference about communities, both static (Dorazio & Royle, 2005; Gelfand et al., 2005; Dorazio et al., 2006; Latimer et al., 2006; Kéry & Royle, 2008a,b) and dynamic (Kéry et al., 2009; Dorazio et al., in press).”
- Plant responses to agricultural intensification (2008, unknown): “(Gelfand et al. 2005).”
- Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities (2017, background): “3; Dorazio and Royle 2005, Gelfand et al. 2005, Dorazio et al. 2006).”
- Colonization and persistence ability explain the extent to which plant species fill their potential range (2007, background): “Higgins et al., 2003; Thuiller et al., 2004; Gelfand et al., 2005; Guisan & Thuiller, 2005).”
- Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches (2011, methodology): “We also produced a richness map by simply summing the mean probabilities (Lehmann et al., 2002; Gelfand et al., 2005) predicted by each technique for each species in each plot (S-SDMsumprob).”
- A structured and dynamic framework to advance traits-based theory and prediction in ecology. (2010, methodology): “…models (Reich et al. 2003), statistical mechanics models (Shipley et al. 2006), Bayesian multilevel models (Gelman et al. 2004; Latimer et al. 2004, 2006; Gelfand et al. 2005, 2006) and dynamical systems models (Norberg et al. 2001; Chesson et al. 2002; Loreau et al. 2003; Savage et al. 2007).”
- Estimating demographic models for the range dynamics of plant species (2010, background): “This database arguably is ‘the largest and highest quality of its kind in the world for studying biodiversity’ (Gelfand et al., 2005).”
- Statistical solutions for error and bias in global citizen science datasets (2014, methodology): “Alternatively, error and bias correction measures can be applied at the species level in a hierarchical model (such as by using a detection–occupancy model) and the diversity indices calculated as a derived parameter (Gelfand et al., 2005; Holt et al., 2013; Kery et al., 2010a).”