Explainable artificial intelligence enhances the ecological interpretability of black‐box species distribution models

Standards for distribution models in biodiversity assessments

(2019) Miguel B. Araújo et al.   Science Advances

Associations between habitat quality, body size and reproductive fitness in the alpine endemic spider Vesubia jugorum

(2019) Stefano Mammola et al.   GLOBAL ECOLOGY AND BIOGEOGRAPHY

Machine Learning Interpretability: A Survey on Methods and Metrics

(2019) Diogo V. Carvalho et al.   Electronics

Definitions, methods, and applications in interpretable machine learning

(2019) W. James Murdoch et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Why understanding the pioneering and continuing contributions of BIOCLIM to species distribution modelling is important

(2018) Trevor H. Booth   AUSTRAL ECOLOGY

Nonlinear higher order abiotic interactions explain riverine biodiversity

(2018) Masahiro Ryo et al.   JOURNAL OF BIOGEOGRAPHY

Wallace : A flexible platform for reproducible modeling of species niches and distributions built for community expansion

(2018) Jamie M. Kass et al.   Methods in Ecology and Evolution

Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI)

(2018) Amina Adadi et al.   IEEE Access

Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics

(2017) Yoan Fourcade et al.   GLOBAL ECOLOGY AND BIOGEOGRAPHY

WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas

(2017) Stephen E. Fick et al.   INTERNATIONAL JOURNAL OF CLIMATOLOGY

Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being

(2017) Gretta T. Pecl et al.   SCIENCE

Statistically reinforced machine learning for nonlinear patterns and variable interactions

(2017) Masahiro Ryo et al.   Ecosphere

Multi-level block permutation

(2015) Anderson M. Winkler et al.   NEUROIMAGE

No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation

(2015) Huijie Qiao et al.   Methods in Ecology and Evolution

Opportunities for improved distribution modelling practice via a strict maximum likelihood interpretation of MaxEnt

(2014) Rune Halvorsen et al.   ECOGRAPHY

What do we gain from simplicity versus complexity in species distribution models?

(2014) Cory Merow et al.   ECOGRAPHY

bioclim: the first species distribution modelling package, its early applications and relevance to most currentMaxEntstudies

(2013) Trevor H. Booth et al.   DIVERSITY AND DISTRIBUTIONS

A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter

(2013) Cory Merow et al.   ECOGRAPHY

Microclimatic challenges in global change biology

(2013) Kristen A. Potter et al.   GLOBAL CHANGE BIOLOGY

Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

(2012) Carsten F. Dormann et al.   ECOGRAPHY

A statistical explanation of MaxEnt for ecologists

(2010) Jane Elith et al.   DIVERSITY AND DISTRIBUTIONS

Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

(2009) Jane Elith et al.   Annual Review of Ecology Evolution and Systematics

Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models

(2009) Jane Elith et al.   ECOGRAPHY

openModeller: a generic approach to species’ potential distribution modelling

(2009) Mauro Enrique de Souza Muñoz et al.   GEOINFORMATICA

Predictive learning via rule ensembles

(2008) Jerome H. Friedman et al.   Annals of Applied Statistics

A working guide to boosted regression trees

(2008) J. Elith et al.   JOURNAL OF ANIMAL ECOLOGY

Machine Learning Methods Without Tears: A Primer for Ecologists

(2008) Julian D. Olden et al.   QUARTERLY REVIEW OF BIOLOGY