THOMAS C. EDWARDS JR.

Ecological Modeling · 2002–2015

A researcher in the Science corpus: 9 papers; trusts 26 researchers and is trusted by 68 researchers here, as inferred from the citation graph.

Subfields

• Ecological Modeling — 116 citations into it; #78 of 2,530 by trust here, #215 by citations
• Nature and Landscape Conservation — 115 citations into it; #203 of 6,941 by trust here, #523 by citations
• Ecology — 79 citations into it; #9,171 of 10,717 by trust here, #3,002 by citations
• Ecology, Evolution, Behavior and Systematics — 33 citations into it; #183 of 6,648 by trust here, #694 by citations
• Statistics and Probability — 32 citations into it
• Artificial Intelligence — 24 citations into it; #29,347 of 35,345 by trust here, #25,809 by citations
• Environmental Engineering — 17 citations into it; #2,534 of 5,479 by trust here, #1,681 by citations
• Plant Science — 11 citations into it

Whom THOMAS C. EDWARDS JR. trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Antoine Guisan
2. Robert Tibshirani
3. Eric R. Ziegel
4. Trevor J. Hastie
5. Michael P. Austin
6. Miguel B. Araújo
7. David Ruppert
8. James A. Hanley
9. Jr. Frank E. Harrell
10. Wilfried Thuiller

Who trusts THOMAS C. EDWARDS JR. overall

1. Antoine Guisan
2. Rune Halvorsen
3. Tomás Václavík
4. Emad Kaky
5. Michael P. Austin
6. A. Jiménez‐Valverde
7. M. Wisz
8. Richard G. Pearson
9. Wilfried Thuiller
10. Janet Franklin

Papers from THOMAS C. EDWARDS JR.'s perspective

Ordered by how strongly the researchers THOMAS C. EDWARDS JR. trusts endorse each paper. Same corpus, a different order for every researcher.

1. Predicting species distribution: offering more than simple habitat models. (Ecology Letters)
2. On the relationship between niche and distribution
3. Generalized Linear Models
4. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling
5. COMPETITIVE INTERACTIONS BETWEEN TREE SPECIES IN NEW ZEALAND'S OLD‐GROWTH INDIGENOUS FORESTS
6. The meaning and use of the area under a receiver operating characteristic (ROC) curve.
7. Generalized Additive Models
8. Evaluating the predictive performance of habitat models developed using logistic regression
9. A Coefficient of Agreement for Nominal Scales
10. Regression Shrinkage and Selection via the Lasso

Their papers

• Machine learning for predicting soil classes in three semi-arid landscapes (2015) — 345 citations
• What do we gain from simplicity versus complexity in species distribution models (2014) — 593 citations
• Co‐occurrence patterns of trees along macro‐climatic gradients and their potential influence on the present and future distribution of Fagus sylvatica L. (2011) — 148 citations
• New trends in species distribution modelling (2010) — 340 citations
• Sampling in ecology and evolution – bridging the gap between theory and practice (2010) — 156 citations
• Colloquium Papers: Climatic extremes improve predictions of spatial patterns of tree species (2009) — 405 citations
• Effects of sample survey design on the accuracy of classification tree models in species distribution models (2006) — 150 citations
• Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods (2006) — 299 citations
• Generalized linear and generalized additive models in studies of species distributions: setting the scene (2002) — 2,332 citations