J. Lahoz‐Monfort

Ecological Modeling · 2013–2020

A researcher in the Science corpus: 11 papers; trusts 47 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Ecological Modeling — 204 citations into it; #1,289 of 2,530 by trust here, #739 by citations
• Nature and Landscape Conservation — 181 citations into it; #3,591 of 6,941 by trust here, #1,991 by citations
• Ecology — 153 citations into it; #6,351 of 10,717 by trust here, #5,577 by citations
• Statistics and Probability — 71 citations into it
• Artificial Intelligence — 52 citations into it
• Global and Planetary Change — 43 citations into it
• Genetics — 30 citations into it
• Information Systems — 10 citations into it

Whom J. Lahoz‐Monfort trusts overall

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

1. Miguel B. Araújo
2. Marc Kéry
3. Wilfried Thuiller
4. Antoine Guisan
5. J. Andrew Royle
6. Eric R. Ziegel
7. David J. Lunn
8. Jane Elith
9. D. MacKenzie
10. Jerome H. Friedman

Who trusts J. Lahoz‐Monfort overall

1. Hanna Meyer

Papers from J. Lahoz‐Monfort's perspective

Ordered by how strongly the researchers J. Lahoz‐Monfort 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. Predictive habitat distribution models in ecology
3. Generalized Linear Models
4. BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global change
5. Maximum entropy modeling of species geographic distributions
6. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling
7. A statistical explanation of MaxEnt for ecologists
8. Measuring the accuracy of diagnostic systems.
9. A working guide to boosted regression trees.
10. Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models

Their papers

• Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference (2018) — 342 citations
• A standard protocol for reporting species distribution models (2020) — 774 citations
• Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models (2020) — 396 citations
• Data Integration for Large-Scale Models of Species Distributions. (2020) — 354 citations
• A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD (2019) — 581 citations
• blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models (2018) — 669 citations
• Conservation technology: The next generation (2018) — 91 citations
• Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure (2017) — 2,280 citations
• Evaluating 318 continental-scale species distribution models over a 60-year prediction horizon: what factors influence the reliability of predictions? (2017) — 100 citations
• Dealing with false‐positive and false‐negative errors about species occurrence at multiple levels (2017) — 123 citations
• Analysing and mapping species range dynamics using occupancy models (2013) — 146 citations