G. Guillera‐Arroita

Ecological Modeling · 2013–2020

A researcher in the Science corpus: 11 papers; trusts 91 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Ecological Modeling — 221 citations into it; #639 of 2,530 by trust here, #692 by citations
• Nature and Landscape Conservation — 202 citations into it; #1,692 of 6,941 by trust here, #1,870 by citations
• Ecology — 169 citations into it; #2,644 of 10,717 by trust here, #4,993 by citations
• Statistics and Probability — 81 citations into it
• Artificial Intelligence — 54 citations into it
• Global and Planetary Change — 44 citations into it
• Genetics — 31 citations into it
• Economics and Econometrics — 25 citations into it

Whom G. Guillera‐Arroita trusts overall

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

1. J. Andrew Royle
2. Marc Kéry
3. D. MacKenzie
4. Antoine Guisan
5. James D. Nichols
6. Steven J. Phillips
7. S. T. Buckland
8. Jane Elith
9. Miguel B. Araújo
10. Ian J. Fiske

Who trusts G. Guillera‐Arroita overall

1. Otso Ovaskainen
2. Damaris Zurell
3. Hanna Meyer
4. A. Norberg
5. Janet Franklin
6. Gleb Tikhonov
7. Kevin S. McKelvey
8. Nick J B Isaac
9. C. König
10. C. Waldock

Papers from G. Guillera‐Arroita's perspective

Ordered by how strongly the researchers G. Guillera‐Arroita trusts endorse each paper. Same corpus, a different order for every researcher.

1. Generalized Linear Models
2. Predicting species distribution: offering more than simple habitat models. (Ecology Letters)
3. Maximum entropy modeling of species geographic distributions
4. Predictive habitat distribution models in ecology
5. A statistical explanation of MaxEnt for ecologists
6. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling
7. Five (or so) challenges for species distribution modelling
8. A working guide to boosted regression trees.
9. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
10. ESTIMATING SITE OCCUPANCY RATES WHEN DETECTION PROBABILITIES ARE LESS THAN ONE (Ecology)

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
• Defining and evaluating predictions of joint species distribution models (2020) — 57 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
• Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure (2017) — 2,280 citations
• Dealing with false‐positive and false‐negative errors about species occurrence at multiple levels (2017) — 123 citations
• Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities (2017) — 396 citations
• Analysing and mapping species range dynamics using occupancy models (2013) — 146 citations