Matthew J. Smith
Ecological Modeling · with Coomes · 2013–2017
A researcher in the Science corpus: 4 papers; trusts 17 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Ecological Modeling — 112 citations into it; #1,128 of 2,530 by trust here, #923 by citations
- Nature and Landscape Conservation — 101 citations into it; #3,149 of 6,941 by trust here, #2,346 by citations
- Ecology — 83 citations into it; #4,629 of 10,717 by trust here, #3,841 by citations
- Genetics — 11 citations into it
Whom Matthew J. Smith trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jane Elith
- Steven J. Phillips
- Antoine Guisan
- Miguel B. Araújo
- Wilfried Thuiller
- Catherine H. Graham
- Miroslav Dud'ik
- Robert Tibshirani
- Michael P. Austin
- A. Townsend Peterson
Who trusts Matthew J. Smith overall
Papers from Matthew J. Smith's perspective
Ordered by how strongly the researchers Matthew J. Smith trusts endorse each paper. Same corpus, a different order for every researcher.
- Predicting species distribution: offering more than simple habitat models. (Ecology Letters)
- Spatial prediction of species distribution: an interface between ecological theory and statistical modelling
- On the relationship between niche and distribution
- Predictive habitat distribution models in ecology
- BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global change
- Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
- New developments in museum-based informatics and applications in biodiversity analysis. (Trends in Ecology & Evolution)
- A statistical explanation of MaxEnt for ecologists
- Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. (Ecological Applications)
- Mapping Species Distributions: Spatial Inference and Prediction
Their papers
- Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications (2017) — 95 citations
- What do we gain from simplicity versus complexity in species distribution models (2014) — 593 citations
- Using species distribution models to inform IUCN Red List assessments (2014) — 157 citations
- The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models (2013) — 616 citations