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Effects of sample size, data quality, and species response in environmental space on modeling species distributions

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Abstract

Context

There have been many studies using species distribution models (SDMs) to predict shifts in species distributions due to environmental changes, but few consider effects of data quantity, data quality, or species response shape. Modeling studies using field-sampled data may be impaired to an unknown degree by lack of knowledge on species’ true relationships with environmental changes.

Objectives

Using simulations with known relationships we assess model predictions, and investigate which models are more sensitive to sample size, detection limit, or species response shape issues when different SDMs are used for predicting species distribution shifts under environmental changes.

Methods

We simulated 16 species response relationships to ecological gradients differing in response shape (skewness and kurtosis) using a generalized β-function. Populations were randomly sampled at different sample sizes and detection limits. Linear discriminant analysis (LDA), multiple logistic regression (MLR), generalized additive models (GAM), boosted regression trees (BRT), random forests (RF), artificial neural networks (ANN), and maximum entropy models (MaxEnt) were developed on sampled datasets and compared for predicting species occurrence. We used these SDMs to predict distribution patterns for virtual species with different response shapes across a real landscape of varying heterogeneity in environmental conditions, and compared them with the probability of occurrence generated by the β-function.

Results

GAM and BRT were sensitive to both sample size and detection limit changes; RF was more affected by detection limit; ANN and MaxEnt were more affected by sample size; LDA and MLR were sensitive to species response shape changes.

Conclusions

Overall, if little is known about species response to environmental changes, ANN is recommended especially for large sample size. If a focal species is likely to occur only in a narrow range of environmental conditions, GAM and BRT are preferred for large good-quality datasets, and GAM tends to perform slightly better under varied data conditions; RF is recommended for limited amounts of good-quality data. If a focal species is likely to be present in a wide range of environmental conditions, MaxEnt is preferred but caution should be taken for small sample size. If the goal is to identify potential distributions of invasive or endangered species but data quantity and quality are very limited, LDA and MLR are recommended as they generally provide reasonable model sensitivity.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This project was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) through both the Canadian Aquatic Invasive Species Network (CAISN) and Discovery Grant programs to DAJ and by the University of Toronto. We thank Marie-Josée Fortin, Charles K. Minns, Brian J. Shuter, Steven C. Walker, Norman D. Yan, Nicholas E. Mandrak, and Derrick T. de Kerckhove for discussions and comments on early versions of this manuscript.

Funding

This project was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) through both the Canadian Aquatic Invasive Species Network (CAISN) and Discovery Grant programs to DAJ and by the University of Toronto.

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Authors and Affiliations

  1. Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, M1C 1A4, Canada

    Lifei Wang

  2. Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, M5S 3B2, Canada

    Lifei Wang & Donald A. Jackson

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  1. Lifei Wang
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  2. Donald A. Jackson
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Contributions

All authors contributed to the study conception and design. Data simulation and analysis were performed by LW. The first draft of the manuscript was written by LW and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Lifei Wang.

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Wang, L., Jackson, D.A. Effects of sample size, data quality, and species response in environmental space on modeling species distributions. Landsc Ecol 38, 4009–4031 (2023). https://doi.org/10.1007/s10980-023-01771-2

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