<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Species distribution modeling (SDM) can be useful for many applied purposes, e.g., mapping and monitoring of rare and endangered species. Sparse presence data are a recurrent, major obstacle to precise modeling of species distributions. Thus, knowing the minimum number of presences required to obtain reliable distribution models is of fundamental importance for applied use of SDM. This study uses a novel approach to assess the critical sample size (CSS) sufficient for an accurate prediction of species distributions with Maximum Entropy Modeling (MaxEnt). Large presence datasets for thirty insect species, ranging from generalists to specialists regarding their responses to main bioclimatic gradients, were used to produce reference distribution models. Models based on replicated subsamples of different size drawn randomly from the full dataset were compared to the reference model using the index of vector similarity distribution models. Models based on replicated subsamples of different size drawn randomly from the full dataset were compared to the reference model using the index of vector similarity (<italic>IVS</italic>). Two thresholds for <italic>IVS</italic> were determined based on comparison of nine reference models to random null models. The threshold values correspond to 0.95 and 0.99 probability that a model outperforms a random null model in terms of similarity to the reference dataset. For 90% of the species, clearly nonrandom models were obtained with less than 10 presence observations, and for 97% of the species with less than 15 presence observations. We conclude that the number of presence observations required to produce nonrandom models is generally low and, accordingly, that even sparse datasets may be useful for distribution modelling.</p></abstract>

Species distribution modeling (SDM) can be useful for many applied purposes, e.g., mapping and monitoring of rare and endangered species. Sparse presence data are a recurrent, major obstacle to precise modeling of species distributions. Thus, knowing the minimum number of presences required to obtain reliable distribution models is of fundamental importance for applied use of SDM. This study uses a novel approach to assess the critical sample size (CSS) sufficient for an accurate prediction of species distributions with Maximum Entropy Modeling (MaxEnt). Large presence datasets for thirty insect species, ranging from generalists to specialists regarding their responses to main bioclimatic gradients, were used to produce reference distribution models. Models based on replicated subsamples of different size drawn randomly from the full dataset were compared to the reference model using the index of vector similarity distribution models. Models based on replicated subsamples of different size drawn randomly from the full dataset were compared to the reference model using the index of vector similarity (IVS). Two thresholds for IVS were determined based on comparison of nine reference models to random null models. The threshold values correspond to 0.95 and 0.99 probability that a model outperforms a random null model in terms of similarity to the reference dataset. For 90% of the species, clearly nonrandom models were obtained with less than 10 presence observations, and for 97% of the species with less than 15 presence observations. We conclude that the number of presence observations required to produce nonrandom models is generally low and, accordingly, that even sparse datasets may be useful for distribution modelling.

How much is enough? Influence of number of presence observations on the performance of species distribution models

The Norwegian Forest and Landscape Institute

Sommerfeltia

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Species distribution modeling (SDM) can be useful for many applied purposes, e.g., mapping and monitoring of rare and endangered species. Sparse presence data...

How much is enough? Influence of number of presence observations on the performance of species distribution models

Published Online: Feb 14, 2019

Page range: 1 - 28

DOI: https://doi.org/10.2478/som-2019-0001

Keywords
Hellinger distance, Maxent, Sample size, Species distribution modeling, Thresholds for critical sample sizes

© 2019 Bente Støa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

How much is enough? Influence of number of presence observations on the performance of species distribution models

Published Online: Feb 14, 2019

Page range: 1 - 28

DOI: https://doi.org/10.2478/som-2019-0001

KeywordsHellinger distance, Maxent, Sample size, Species distribution modeling, Thresholds for critical sample sizes

© 2019 Bente Støa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Hellinger distance, Maxent, Sample size, Species distribution modeling, Thresholds for critical sample sizes