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Quality of presence data determines species distribution model performance: a novel index to evaluate data quality

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Abstract

Context

Species distribution models (SDMs) are widely used to estimate species’ potential distribution at landscape to regional scales. However, the quality of occurrence data is often compromised by sampling bias, which could raise serious concerns on model accuracy.

Objectives

We propose a model-independent composite measure—representativeness and completeness (RAC) index—to evaluate the quality of species occurrence data. We demonstrate (1) the impact of spatial data quality as measured by RAC on model performance and (2) the feasibility of applying RAC in actual modeling process.

Methods

By using a set of computational experiments on a virtual species, we calculated RAC values for a set of occurrence data representing different degrees of sampling biases. We evaluated model performance (reliability and accuracy) and associated model performance with RAC values. Two case studies were also conducted to demonstrate the association between RAC and model performance.

Results

Model reliability stabilizes when RAC reaches a threshold of 0.4. Model accuracy stabilizes when RAC reaches 0.4 or 0.5 for models with or without complete predictors, respectively. Model performance is more sensitive to data completeness than representativeness. Our case studies further demonstrated that RAC value is closely related to model performance.

Conclusions

Performance of SDMs is closely related to the quality of species occurrence data, which can be measured by our RAC index. We recommend a minimum RAC value of 0.4 for reliable and accurate SDM predictions. To improve prediction accuracy, sampling with multiple centers in a systematic fashion across the environmental space is desired.

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Acknowledgments

We thank Drs. Janet Franklin, Jeffrey Dukes, Jane Frankenberger for helpful comments on an earlier versions of the manuscript. We acknowledge funding support from the National Science Foundation (Macrosystems Biology 1241932).

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Authors and Affiliations

  1. Department of Forestry and Natural Resources, Purdue University, PFEN221E, 715 W. State St., West Lafayette, IN, 47907, USA

    Songlin Fei & Feng Yu

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  1. Songlin Fei
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  2. Feng Yu
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Correspondence to Songlin Fei.

Additional information

Special issue: Macrosystems ecology: Novel methods and new understanding of multi-scale patterns and processes.

Guest Editors: S. Fei, Q. Guo, and K. Potter.

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Fei, S., Yu, F. Quality of presence data determines species distribution model performance: a novel index to evaluate data quality. Landscape Ecol 31, 31–42 (2016). https://doi.org/10.1007/s10980-015-0272-7

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