R. O’Hara
Nature and Landscape Conservation · 2002–2020
A researcher in the Science corpus: 13 papers; trusts 41 researchers and is trusted by 37 researchers here, as inferred from the citation graph.
Subfields
- Nature and Landscape Conservation — 200 citations into it; #436 of 6,941 by trust here, #614 by citations
- Ecological Modeling — 188 citations into it; #351 of 2,530 by trust here, #320 by citations
- Ecology — 153 citations into it; #2,299 of 10,717 by trust here, #2,697 by citations
- Ecology, Evolution, Behavior and Systematics — 87 citations into it
- Statistics and Probability — 57 citations into it; #2,922 of 12,932 by trust here, #2,477 by citations
- Global and Planetary Change — 49 citations into it
- Artificial Intelligence — 33 citations into it; #13,265 of 35,345 by trust here, #21,316 by citations
- Plant Science — 24 citations into it; #951 of 8,232 by trust here, #6,488 by citations
Whom R. O’Hara trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Eric R. Ziegel
- Joseph Hilbe
- Simon N. Wood
- Andrew Gelman
- Peter J. Green
- Michael E. Tipping
- Mário A. T. Figueiredo
- Aaron M. Christ
- Sudipto Banerjee
- William N. Venables
Who trusts R. O’Hara overall
- Anthony R. Ives
- A. Chao
- U. Knief
- Shinichi Nakagawa
- Jan Beck
- Juho Piironen
- Gordon Burtch
- X. Harrison
- Wolfgang Forstmeier
- Olivier Gimenez
Papers from R. O’Hara's perspective
Ordered by how strongly the researchers R. O’Hara trusts endorse each paper. Same corpus, a different order for every researcher.
- Generalized Linear Models
- Bayesian measures of model complexity and fit
- Regression Shrinkage and Selection via the Lasso
- Estimating the Dimension of a Model
- Predicting species distribution: offering more than simple habitat models. (Ecology Letters)
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- Predictive habitat distribution models in ecology
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
- Subset Selection in Regression
Their papers
- A review of Bayesian variable selection methods: what, how and which (2009) — 812 citations
- Do not log‐transform count data (2010) — 1,282 citations
- Data Integration for Large-Scale Models of Species Distributions. (2020) — 354 citations
- Standards for distribution models in biodiversity assessments (2019) — 988 citations
- Moving in the Anthropocene: Global reductions in terrestrial mammalian movements (2018) — 1,095 citations
- So Many Variables: Joint Modeling in Community Ecology. (2015) — 802 citations
- Understanding co‐occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM) (2014) — 646 citations
- Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents (2012) — 442 citations
- A physiological analogy of the niche for projecting the potential distribution of plants (2012) — 83 citations
- How to understand species’ niches and range dynamics: a demographic research agenda for biogeography (2012) — 317 citations
- How to Make Models Add Up — A Primer on GLMMs (2009) — 62 citations
- Species richness estimators: how many species can dance on the head of a pin? (2005) — 219 citations
- BAYESIAN ANALYSIS OF METAPOPULATION DATA (2002) — 113 citations