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Abstract AimSpecies occurrence data are valuable information that enables one to estimate geographical distributions, characterize niches and their evolution, and guide spatial conservation planning. Rapid increases in species occurrence data stem from increasing digitization and aggregation efforts, and citizen science initiatives. However, persistent quality issues in occurrence data can impact the accuracy of scientific findings, underscoring the importance of filtering erroneous occurrence records in biodiversity analyses. InnovationWe introduce an R package, occTest, that synthesizes a growing open‐source ecosystem of biodiversity cleaning workflows to prepare occurrence data for different modelling applications. It offers a structured set of algorithms to identify potential problems with species occurrence records by employing a hierarchical organization of multiple tests. The workflow has a hierarchical structure organized in testPhases(i.e. cleaning vs. testing)that encompass different testBlocksgrouping differenttestTypes(e.g.environmental outlier detection), which may use differenttestMethods(e.g.Rosner test, jacknife,etc.). Four differenttestBlockscharacterize potential problems in geographic, environmental, human influence and temporal dimensions. Filtering and plotting functions are incorporated to facilitate the interpretation of tests. We provide examples with different data sources, with default and user‐defined parameters. Compared to other available tools and workflows, occTest offers a comprehensive suite of integrated tests, and allows multiple methods associated with each test to explore consensus among data cleaning methods. It uniquely incorporates both coordinate accuracy analysis and environmental analysis of occurrence records. Furthermore, it provides a hierarchical structure to incorporate future tests yet to be developed. Main conclusionsoccTest will help users understand the quality and quantity of data available before the start of data analysis, while also enabling users to filter data using either predefined rules or custom‐built rules. As a result, occTest can better assess each record's appropriateness for its intended application.
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AgeStrucNb: Software for Simulating and Detecting Changes in the Effective Number of Breeders (Nb)
Abstract Estimation of the effective number of breeders per reproductive event (Nb) using single sample DNA-marker-based methods has rapidly grown in recent years. However, estimating Nb is difficult in age-structured populations because the performance of estimators is influenced by the Nb / Ne ratio, which varies among species with different life histories. We provide a computer program, AgeStrucNb, to simulate age-structured populations (including life history) and also estimate Nb. The AgeStrucNb program is composed of 4 major components to simulate, subsample, estimate, and then visualize Nb time series data. AgeStrucNb allows users to also quantify the precision and accuracy of any set of loci or sample size to estimate Nb for many species and populations. AgeStrucNb allows users to conduct power analysis to evaluate sensitivity to detect changes in Nb or the power to detect a correlation between trends in Nb and environmental variables (e.g., temperature, habitat quality, predator or pathogen abundance) that could be driving changes in Nb. The software provides Nb estimates for empirical data sets using the LDNe (linkage disequilibrium) method, includes publication-quality output graphs, and outputs genotype files in Genepop format for use in other programs. AgeStrucNb will help advance the application of genetic markers for monitoring Nb, which will help biologists to detect population declines and growth, which is crucial for research and conservation of natural and managed populations.
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- Award ID(s):
- 1639014
- PAR ID:
- 10199984
- Editor(s):
- Sherwin, William
- Date Published:
- Journal Name:
- Journal of Heredity
- Volume:
- 111
- Issue:
- 5
- ISSN:
- 0022-1503
- Page Range / eLocation ID:
- 491 to 497
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/jhered/esaa028
