Machine Learning Methods Without Tears: A Primer for Ecologists

By Julian D. Olden, Joshua J. Lawler, N. LeRoy Poff. The Quarterly review of biology, 2008

762 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecological Modeling, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/298af26244e3ad836c1aa5cf5855d05f5197063d

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Citations in the corpus

Of mice and mammoths: generality and antiquity of the island rule (2013, background): “Even for extant species of mammals, however, there remains some debate regarding both the generality and, in particular, the causality of the island rule pattern (Roth, 2001; Palkovacs, 2003; Meiri et al., 2004a,b, 2008; Durst & Roth, 2012; Lomolino et al., 2012).”
Modeling Species Distribution and Change Using Random Forest (2011, background): “Well-defined issues in ecological modeling, such as complex non-linear interactions, spatial autocorrelation, high-dimensionality, non-stationary, historic signal, anisotropy, and scale contribute to problems that the frequentist data model has difficulty addressing (Olden et al. 2008).”
Statistical solutions for error and bias in global citizen science datasets (2014, methodology): “…that have 351 been applied in an ecological setting include classification and regression 352 trees (CART, De’ath, 2000), boosted regression trees (BRT, Elith et al., 353 2008), random forests (Cutler et al., 2007), artificial neural networks, and 354 genetic algorithms (Olden et al., 2008).”
Random Forests for Global and Regional Crop Yield Predictions (2016, methodology): “Machine-learning techniques have been used increasingly in recent years as niche-based classification modeling tools for predicting species habitat suitability in response to climate change [6, 7].”
Using species distribution models to infer potential climate change-induced range shifts of freshwater fish in south-eastern Australia (2011, background): “…underlying model structure than do traditional modelling tools and are well suited to capturing non-linear relationships and interactions among predictors, therefore lending themselves to generating novel insights into species–environment relationships (Olden et al. 2008; Elith and Leathwick 2009).”
Local and landscape drivers of arthropod abundance, richness, and trophic composition in urban habitats (2013, background): “In addition, PC1 was positively correlated with concrete, building, and grass cover, height of the herbaceous layer, and with increases in developed area.”
GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran (2015, background): “The ML technique is a rapidly growing area of predictive modeling that is concerned with identifying structure in complex, often nonlinear, data and generating accurate predictive models (Olden et al. 2008).”
Estimating population size of a nocturnal burrow-nesting seabird using acoustic monitoring and habitat mapping (2014, methodology): “…with estimated nest density), we used a powerful machine-learning algorithm based on ensembles of regression trees (Random Forest) to predict nest density for each combination of habitat features that existed along the coast of Corvo (Cutler et al. 2007; Hochachka et al. 2007; Olden et al. 2008).”
Use of niche models in invasive species risk assessments (2011, methodology): “Three extreme cases are statistical methods (e.g., generalized linear models, generalized additive models, classification and regression trees, etc.; see Guisan and Zimmermann 2000 for an overview); machine-learning methods (e.g., Maxent, Phillips et al. 2006; GARP, Stockwell and Peters 1999; artifi”
A review of machine learning applications in wildfire science and management (2020, positive): “The motivations for using AI for forested ecosystem related research, including disturbances due to wildfire, insects, and disease, were discussed in an early paper Schmoldt (2001), while Olden, Lawler, and Poff (2008) further argued for the use of ML methods to model complex problems in ecology.”