Dataset Information

Raritas: a program for counting high diversity categorical data with highly unequal abundances.

ABSTRACT: Acquiring data on the occurrences of many types of difficult to identify objects are often still made by human observation, for example, in biodiversity and paleontologic research. Existing computer counting programs used to record such data have various limitations, including inflexibility and cost. We describe a new open-source program for this purpose-Raritas. Raritas is written in Python and can be run as a standalone app for recent versions of either MacOS or Windows, or from the command line as easily customized source code. The program explicitly supports a rare category count mode which makes it easier to collect quantitative data on rare categories, for example, rare species which are important in biodiversity surveys. Lastly, we describe the file format used by Raritas and propose it as a standard for storing geologic biodiversity data. 'Stratigraphic occurrence data' file format combines extensive sample metadata and a flexible structure for recording occurrence data of species or other categories in a series of samples.

SUBMITTER: Lazarus DB

PROVIDER: S-EPMC6183511 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Raritas: a program for counting high diversity categorical data with highly unequal abundances.

Lazarus David B DB Renaudie Johan J Lenz Dorina D Diver Patrick P Klump Jens J

PeerJ 20181009

Acquiring data on the occurrences of many types of difficult to identify objects are often still made by human observation, for example, in biodiversity and paleontologic research. Existing computer counting programs used to record such data have various limitations, including inflexibility and cost. We describe a new open-source program for this purpose-Raritas. Raritas is written in Python and can be run as a standalone app for recent versions of either MacOS or Windows, or from the command li ...[more]

PMID: 30324008

Similar Datasets

Project description:BackgroundIncomplete categorical variables with more than two categories are common in public health data. However, most of the existing missing-data methods do not use the information from nonresponse (missingness) probabilities.MethodsWe propose a nearest-neighbour multiple imputation approach to impute a missing at random categorical outcome and to estimate the proportion of each category. The donor set for imputation is formed by measuring distances between each missing value with other non-missing values. The distance function is calculated based on a predictive score, which is derived from two working models: one fits a multinomial logistic regression for predicting the missing categorical outcome (the outcome model) and the other fits a logistic regression for predicting missingness probabilities (the missingness model). A weighting scheme is used to accommodate contributions from two working models when generating the predictive score. A missing value is imputed by randomly selecting one of the non-missing values with the smallest distances. We conduct a simulation to evaluate the performance of the proposed method and compare it with several alternative methods. A real-data application is also presented.ResultsThe simulation study suggests that the proposed method performs well when missingness probabilities are not extreme under some misspecifications of the working models. However, the calibration estimator, which is also based on two working models, can be highly unstable when missingness probabilities for some observations are extremely high. In this scenario, the proposed method produces more stable and better estimates. In addition, proper weights need to be chosen to balance the contributions from the two working models and achieve optimal results for the proposed method.ConclusionsWe conclude that the proposed multiple imputation method is a reasonable approach to dealing with missing categorical outcome data with more than two levels for assessing the distribution of the outcome. In terms of the choices for the working models, we suggest a multinomial logistic regression for predicting the missing outcome and a binary logistic regression for predicting the missingness probability.

Project description:In conservation studies, solely widespread species are often used as indicators of diversity patterns, but narrow-ranged species can show different patterns. Here, we assess how well subsets of narrow-ranged, widespread or randomly selected plant species represent patterns of species richness and weighted endemism in Gabon, tropical Africa. Specifically, we assess the effect of using different definitions of widespread and narrow-ranged and of the information content of the subsets. Finally, we test if narrow-ranged species are overrepresented in species-rich areas. Based on distribution models of Gabonese plant species, we defined sequential subsets from narrow-ranged-to-widespread, widespread-to-narrow-ranged, and 100 randomly arranged species sequences using the range sizes of species in tropical Africa and within Gabon. Along these sequences, correlations between subsets and the total species richness and total weighted endemism patterns were computed. Random species subsets best represent the total species richness pattern, whereas subsets of narrow-ranged species best represent the total weighted endemism pattern. For species ordered according to their range sizes in tropical Africa, subsets of narrow-ranged species represented the total species richness pattern better than widespread species subsets did. However, the opposite was true when range sizes were truncated by the Gabonese national country borders. Correcting for the information content of the subset results in a skew of the sequential correlations, its direction depending on the range-size frequency distribution. Finally, we find a strong, positive, non-linear relation between weighted endemism and total species richness. Observed differences in the contribution of narrow-ranged, widespread and randomly selected species to species richness and weighted endemism patterns can be explained by the range-size frequency distribution and the use of different definitions of widespread or narrow-ranged. We call for a reconsideration of the use of widespread species as an indicator of diversity patterns, and advocate using the full ranges of species when assessing diversity patterns.

Dataset Information

Raritas: a program for counting high diversity categorical data with highly unequal abundances.

Publications

Raritas: a program for counting high diversity categorical data with highly unequal abundances.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets