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Spatial bias in the GBIF database and its effect on modeling species' geographic distributions

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TLDR
A subsampling routine is used as an exemplar taxon to provide evidence that range model quality is decreasing due to the spatial clustering of distributional records in GBIF and shows that data with less spatial bias produce better predictive models even though they are based on less input data.
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This article is published in Ecological Informatics.The article was published on 2014-01-01. It has received 424 citations till now.

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Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.

TL;DR: The ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species, but the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases.
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Recommending plant taxa for supporting on-site species identification

TL;DR: It is found that occurrence records are complementary to presence-absence data and using both in combination yields considerably higher recall of 96% along with improved ranking metrics, and a spatio-temporal prior can substantially expedite the overall identification problem.
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Worldwide occurrence records suggest a global decline in bee species richness

TL;DR: In this article, the authors analyzed publicly available worldwide occurrence records from the Global Biodiversity Information Facility spanning over a century and found that after the 1990s, the number of collected bee species declines steeply such that approximately 25% fewer species were reported between 2006 and 2015 than before 1990s.
References
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Journal ArticleDOI

Troubling Trends in Scientific Software Use

TL;DR: This work describes problems with the adoption and use of scientific software and reveals key insights and best practices for how to develop, standardize, and implement software.
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What ' s on the horizon for macroecology?

TL;DR: Scanning the horizon of macroecology, it is identified that more sophisticated methods are needed to account for the biases inherent to sampling at large scale and that Bayesian methods may be particularly suitable to address these challenges.
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PAPER Geographical sampling bias in a large distributional database and its effects on species richness-environment models

TL;DR: In this article, the authors quantify the inventory incompleteness of vascular plant data across 2377 Chinese counties and test whether inventory incompletteness affects the analysis of richness-environment relationships and spatial predictions of species richness.
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MaxEnt versus MaxLike: empirical comparisons with ant species distributions

TL;DR: For species distribution modeling, MaxLike, and similar models that are based on an explicit sampling process and that directly estimate probability of occurrence, should be considered as important alternatives to the widely-used MaxEnt framework.
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Online solutions and the ‘Wallacean shortfall’: what does GBIF contribute to our knowledge of species' ranges?

TL;DR: Although GBIF contributed relevant additional information, it is not yet an effective alternative to manual compilation and databasing of distributional records from collections and literature sources, at least in lesser-known taxa such as invertebrates.
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