Abstract
Spatial records of species are commonly misidentified, which can change the predicted distribution of a species obtained from a species distribution model (SDM). Experiments were undertaken to predict the distribution of real and simulated species using MaxEnt and presence-only data “contaminated” with varying rates of misidentification error. Additionally, the difference between the niche of the target and contaminating species was varied. The results show that species misidentification errors may act to contract or expand the predicted distribution of a species while shifting the predicted distribution towards that of the contaminating species. Furthermore the magnitude of the effects was positively related to the ecological distance between the species’ niches and the size of the error rates. Critically, the magnitude of the effects was substantial even when using small error rates, smaller than common average rates reported in the literature, which may go unnoticed while using a standard evaluation method, such as the area under the receiver operating characteristic curve. Finally, the effects outlined were shown to impact negatively on practical applications that use SDMs to identify priority areas, commonly selected for various purposes such as management. The results highlight that species misidentification should not be neglected in species distribution modeling.
Highlights
Spatial patterns of species presence have been a central theme for long time in a variety of disciplines, such as ecology, biogeography, evolution, and management
Contraction tended to occur only when the contaminating species was less dispersed than the target species while, expansion tended to occur when the contaminating species was more dispersed than the target species
With regard to the real data, the predicted distribution of the target species C. cooperi contracted when modeled with contaminating records of C. medullaris (Figure 3a), which has a limited distribution in São Miguel
Summary
Spatial patterns of species presence have been a central theme for long time in a variety of disciplines, such as ecology, biogeography, evolution, and management. Barry and Elith [4] classify the sources of error and uncertainty embedded in SDMs into two main categories: deficiencies in the data and deficiencies introduced by the specification of the model. Common problems include missing variables [4], small sample size [5,6], biased samples [7], incorrectly located species records [6], lack of absence records [8], and disagreement between the scale (grain/extent) of the species data and the modeling setup [1,6,9]. The second category includes possible discrepancies between the model used and the “true” model (e.g., if the model used is linear and the true relationship between species presence and variables is quadratic) and the modeling approach (e.g., envelope, distance-based, and regression) [4]
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