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Use of Anecdotal Occurrence Data in Species Distribution Models: An Example Based on the White-Nosed Coati (Nasua narica) in the American Southwest.

Frey JK, Lewis JC, Guy RK, Stuart JN - Animals (Basel) (2013)

Bottom Line: We found that the predicted distribution of the coati based on datasets that included anecdotal occurrence records were similar to those based on datasets that only included physical evidence.We concluded that occurrence datasets that include anecdotal records can be used to infer species distributions, providing such data are used only for easily-identifiable species and based on robust modeling methods such as maximum entropy.Use of a reliability rating system is critical for using anecdotal data.

View Article: PubMed Central - PubMed

Affiliation: Department of Fish, Wildlife and Conservation Ecology, New Mexico State University, Las Cruces, NM 88003, USA. jfrey@nmsu.edu.

ABSTRACT
Species distributions are usually inferred from occurrence records. However, these records are prone to errors in spatial precision and reliability. Although influence of spatial errors has been fairly well studied, there is little information on impacts of poor reliability. Reliability of an occurrence record can be influenced by characteristics of the species, conditions during the observation, and observer's knowledge. Some studies have advocated use of anecdotal data, while others have advocated more stringent evidentiary standards such as only accepting records verified by physical evidence, at least for rare or elusive species. Our goal was to evaluate the influence of occurrence records with different reliability on species distribution models (SDMs) of a unique mammal, the white-nosed coati (Nasua narica) in the American Southwest. We compared SDMs developed using maximum entropy analysis of combined bioclimatic and biophysical variables and based on seven subsets of occurrence records that varied in reliability and spatial precision. We found that the predicted distribution of the coati based on datasets that included anecdotal occurrence records were similar to those based on datasets that only included physical evidence. Coati distribution in the American Southwest was predicted to occur in southwestern New Mexico and southeastern Arizona and was defined primarily by evenness of climate and Madrean woodland and chaparral land-cover types. Coati distribution patterns in this region suggest a good model for understanding the biogeographic structure of range margins. We concluded that occurrence datasets that include anecdotal records can be used to infer species distributions, providing such data are used only for easily-identifiable species and based on robust modeling methods such as maximum entropy. Use of a reliability rating system is critical for using anecdotal data.

No MeSH data available.


Related in: MedlinePlus

Comparison of subsets of occurrence records that differ in reliability and precision (see Table 3, Table 4) for the white-nosed coati (Nasua narica) in Arizona and New Mexico: (A) very conservative, (B) conservative, (C) best a priori, (D) moderate, (E) liberal, (F) poor precision, (G) poor reliability.
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animals-03-00327-f001: Comparison of subsets of occurrence records that differ in reliability and precision (see Table 3, Table 4) for the white-nosed coati (Nasua narica) in Arizona and New Mexico: (A) very conservative, (B) conservative, (C) best a priori, (D) moderate, (E) liberal, (F) poor precision, (G) poor reliability.

Mentions: Occurrence records exhibit simultaneous errors in reliability and spatial precision (Table 2). Species distribution models based on maximum entropy methods have been shown to be robust to location errors up to at least 5 km (using 100 m spatial resolution of environmental layer), with the upper limit unknown [11]. Further, deletion of records with large spatial error reduces sample size, which can negatively impact the SDM [27]. Consequently, spatial errors are rarely acknowledged or controlled for in species distribution modeling even though it is recognized that some museum data may have large errors [11]. Regardless, the influence of spatial error on SDMs has not been extensively evaluated and we wanted to insure that our results accounted for this error. Because this study was based on real data, it was not possible to hold precision constant for all reliability classes. Consequently, we created seven different datasets of occurrence records, that varied in reliability and precision, upon which the SDMs were based (Figure 1, Table 3, Table 4).


Use of Anecdotal Occurrence Data in Species Distribution Models: An Example Based on the White-Nosed Coati (Nasua narica) in the American Southwest.

Frey JK, Lewis JC, Guy RK, Stuart JN - Animals (Basel) (2013)

Comparison of subsets of occurrence records that differ in reliability and precision (see Table 3, Table 4) for the white-nosed coati (Nasua narica) in Arizona and New Mexico: (A) very conservative, (B) conservative, (C) best a priori, (D) moderate, (E) liberal, (F) poor precision, (G) poor reliability.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4494388&req=5

animals-03-00327-f001: Comparison of subsets of occurrence records that differ in reliability and precision (see Table 3, Table 4) for the white-nosed coati (Nasua narica) in Arizona and New Mexico: (A) very conservative, (B) conservative, (C) best a priori, (D) moderate, (E) liberal, (F) poor precision, (G) poor reliability.
Mentions: Occurrence records exhibit simultaneous errors in reliability and spatial precision (Table 2). Species distribution models based on maximum entropy methods have been shown to be robust to location errors up to at least 5 km (using 100 m spatial resolution of environmental layer), with the upper limit unknown [11]. Further, deletion of records with large spatial error reduces sample size, which can negatively impact the SDM [27]. Consequently, spatial errors are rarely acknowledged or controlled for in species distribution modeling even though it is recognized that some museum data may have large errors [11]. Regardless, the influence of spatial error on SDMs has not been extensively evaluated and we wanted to insure that our results accounted for this error. Because this study was based on real data, it was not possible to hold precision constant for all reliability classes. Consequently, we created seven different datasets of occurrence records, that varied in reliability and precision, upon which the SDMs were based (Figure 1, Table 3, Table 4).

Bottom Line: We found that the predicted distribution of the coati based on datasets that included anecdotal occurrence records were similar to those based on datasets that only included physical evidence.We concluded that occurrence datasets that include anecdotal records can be used to infer species distributions, providing such data are used only for easily-identifiable species and based on robust modeling methods such as maximum entropy.Use of a reliability rating system is critical for using anecdotal data.

View Article: PubMed Central - PubMed

Affiliation: Department of Fish, Wildlife and Conservation Ecology, New Mexico State University, Las Cruces, NM 88003, USA. jfrey@nmsu.edu.

ABSTRACT
Species distributions are usually inferred from occurrence records. However, these records are prone to errors in spatial precision and reliability. Although influence of spatial errors has been fairly well studied, there is little information on impacts of poor reliability. Reliability of an occurrence record can be influenced by characteristics of the species, conditions during the observation, and observer's knowledge. Some studies have advocated use of anecdotal data, while others have advocated more stringent evidentiary standards such as only accepting records verified by physical evidence, at least for rare or elusive species. Our goal was to evaluate the influence of occurrence records with different reliability on species distribution models (SDMs) of a unique mammal, the white-nosed coati (Nasua narica) in the American Southwest. We compared SDMs developed using maximum entropy analysis of combined bioclimatic and biophysical variables and based on seven subsets of occurrence records that varied in reliability and spatial precision. We found that the predicted distribution of the coati based on datasets that included anecdotal occurrence records were similar to those based on datasets that only included physical evidence. Coati distribution in the American Southwest was predicted to occur in southwestern New Mexico and southeastern Arizona and was defined primarily by evenness of climate and Madrean woodland and chaparral land-cover types. Coati distribution patterns in this region suggest a good model for understanding the biogeographic structure of range margins. We concluded that occurrence datasets that include anecdotal records can be used to infer species distributions, providing such data are used only for easily-identifiable species and based on robust modeling methods such as maximum entropy. Use of a reliability rating system is critical for using anecdotal data.

No MeSH data available.


Related in: MedlinePlus