The interplay of various sources of noise on reliability of species distribution models hinges on ecological specialisation

Digitized species occurrence data provide an unprecedented source of information for ecologists and conservationists. Species distribution model (SDM) has become a popular method to utilise these data for understanding the spatial and temporal distribution of species, and for modelling biodiversity...

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Bibliographic Details
Published in:PloS one Vol. 12; no. 11; p. e0187906
Main Authors: Soultan, Alaaeldin, Safi, Kamran
Format: Journal Article
Language:English
Published: United States Public Library of Science 13.11.2017
Public Library of Science (PLoS)
Subjects:
Accuracy
Algorithms
Biodiversity
Biological diversity
Biology and Life Sciences
Correlation coefficient
Correlation coefficients
Digitization
Ecology
Ecology and Environmental Sciences
Environmental conditions
Environmentalists
Hinges
Mathematical models
Models, Theoretical
Niche overlap
Noise
Ornithology
Performance assessment
Physical Sciences
Predictions
Principal Component Analysis
Reliability analysis
Reproducibility of Results
Research and Analysis Methods
Spatial data
Specialization
Species
Species Specificity
Studies
Technology application
Temporal distribution
Germany
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:Digitized species occurrence data provide an unprecedented source of information for ecologists and conservationists. Species distribution model (SDM) has become a popular method to utilise these data for understanding the spatial and temporal distribution of species, and for modelling biodiversity patterns. Our objective is to study the impact of noise in species occurrence data (namely sample size and positional accuracy) on the performance and reliability of SDM, considering the multiplicative impact of SDM algorithms, species specialisation, and grid resolution. We created a set of four 'virtual' species characterized by different specialisation levels. For each of these species, we built the suitable habitat models using five algorithms at two grid resolutions, with varying sample sizes and different levels of positional accuracy. We assessed the performance and reliability of the SDM according to classic model evaluation metrics (Area Under the Curve and True Skill Statistic) and model agreement metrics (Overall Concordance Correlation Coefficient and geographic niche overlap) respectively. Our study revealed that species specialisation had by far the most dominant impact on the SDM. In contrast to previous studies, we found that for widespread species, low sample size and low positional accuracy were acceptable, and useful distribution ranges could be predicted with as few as 10 species occurrences. Range predictions for narrow-ranged species, however, were sensitive to sample size and positional accuracy, such that useful distribution ranges required at least 20 species occurrences. Against expectations, the MAXENT algorithm poorly predicted the distribution of specialist species at low sample size.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0187906