Use of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook

Huettmann, Falk; Craig, Erica H.; Herrick, Keiko A.; Baltensperger, Andrew P.; Humphries, Grant R. W.; Lieske, David J.; Miller, Katharine; Mullet, Timothy C.; Oppel, Steffen; Resendiz, Cynthia; Rutzen, Imme; Schmid, Moritz S.; Suwal, Madan Krishna; Young, Brian

doi:10.1007/978-3-319-96978-7_2

Cited by 14 publications

(10 citation statements)

References 130 publications

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“…Furthermore, we here demonstrated the effectiveness of the three ML algorithms for multicollinearity of predictors for species distribution models (SDMs) with only one case study; thus, future studies may need to test and confirm the effectiveness of ML algorithms for multicollinearity in SDMs for different data and different ecosystems. Support vector machines are less popular than MaxEnt and RFs in the literature of species distribution models (Huettmann et al, 2018). Support vector machines generalize the inference/classification results only on the Vapnik-Chervonenkis (VC) dimension h, a reduced dimensionality of input data, to achieve sparsity.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, the deterministic approaches may make SVMs faster and less costly in computation than RFs. Support vector machines are less popular than MaxEnt and RFs in the literature of species distribution models (Huettmann et al, 2018). Future studies may consider single-class SVMs, a variant of SVMs for single-class data, as a true presence-only model for estimating species distributions (Mack & Waske, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Ecological studies have not exploited extensively the excellent performances of SVMs in pattern identification and recognition and the capacity to analyze large amounts of data and complex relationships (Huettmann et al, 2018). Ecological studies have not exploited extensively the excellent performances of SVMs in pattern identification and recognition and the capacity to analyze large amounts of data and complex relationships (Huettmann et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Second, we compared predictive performances of MaxEnt, RFs, and SVMs between correlation removal and principal component approaches to multicollinearity. Ecological studies have not exploited extensively the excellent performances of SVMs in pattern identification and recognition and the capacity to analyze large amounts of data and complex relationships (Huettmann et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning of large‐scale spatial distributions of wild turkeys with high‐dimensional environmental data

Farrell

Wang

Rush

et al. 2019

Ecology and Evolution

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Species distribution modeling often involves high‐dimensional environmental data. Large amounts of data and multicollinearity among covariates impose challenges to statistical models in variable selection for reliable inferences of the effects of environmental factors on the spatial distribution of species. Few studies have evaluated and compared the performance of multiple machine learning (ML) models in handling multicollinearity. Here, we assessed the effectiveness of removal of correlated covariates and regularization to cope with multicollinearity in ML models for habitat suitability. Three machine learning algorithms maximum entropy (MaxEnt), random forests (RFs), and support vector machines (SVMs) were applied to the original data (OD) of 27 landscape variables, reduced data (RD) with 14 highly correlated covariates being removed, and 15 principal components (PC) of the OD accounting for 90% of the original variability. The performance of the three ML models was measured with the area under the curve and continuous Boyce index. We collected 663 nonduplicated presence locations of Eastern wild turkeys (Meleagris gallopavo silvestris) across the state of Mississippi, United States. Of the total locations, 453 locations separated by a distance of ≥2 km were used to train the three ML algorithms on the OD, RD, and PC data, respectively. The remaining 210 locations were used to validate the trained ML models to measure ML performance. Three ML models had excellent performance on the RD and PC data. MaxEnt and SVMs had good performance on the OD data, indicating the adequacy of regularization of the default setting for multicollinearity. Weak learning of RFs through bagging appeared to alleviate multicollinearity and resulted in excellent performance on the OD data. Regularization of ML algorithms may help exploratory studies of the effects of environmental factors on the spatial distribution and habitat suitability of wildlife.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning of large‐scale spatial distributions of wild turkeys with high‐dimensional environmental data

Farrell

Wang

Rush

et al. 2019

Ecology and Evolution

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“…Moreover, they do not require the a priori assumptions used by the process-based models [12]. Additionally, ML models can be applied to virtually any data, eliminating the need to specify assumptions regarding the underlying statistical distribution of the data, as in traditional statistical models [17]. Despite the many advantages of ML methods, there has been a large amount of work to establish HAB dynamics using process-based and traditional statistical models.…”

Section: Forecasting Decision Making Time-series Data Machine Learningmentioning

confidence: 99%

A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination

Cruz

Costa

Vinga

et al. 2021

JMSE

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Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities.

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Machine learning in landscape ecological analysis: a review of recent approaches

et al. 2021

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Use of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook

Cited by 14 publications

References 130 publications

Machine learning of large‐scale spatial distributions of wild turkeys with high‐dimensional environmental data

Machine learning of large‐scale spatial distributions of wild turkeys with high‐dimensional environmental data

A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination

Machine learning in landscape ecological analysis: a review of recent approaches

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