Distorted Views of Biodiversity: Spatial and Temporal Bias in Species Occurrence Data

By E. Boakes, P. McGowan, Richard A. Fuller, C. Ding, Natalie E. Clark, K. O'Connor, Georgina M. Mace. PLoS Biology, 2010

751 citations recorded by the source corpus.

Boakes et al. compile and analyze a historical dataset of 170,000 bird sightings over two centuries and show how changing trends in data gathering may confound a true picture of biodiversity change.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/f219be7aa76ff019fa334a6e103df12936374cd2

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Citations in the corpus

Sampling biases shape our view of the natural world (2020, background): “Whilst former analyses have explored some of these analyses, including geographic, political and accessibility biases (Kadmon et al. 2004, Boakes et al. 2010, Amano and Sutherland 2013, Meyer et al. 2016, Moudrý and Devillers 2020) most of these focus on limited regions or taxa (the largest…”
How does climate change cause extinction? (2013, neutral): “Furthermore, resurveys should account for false absences that may be misinterpreted as extinctions and for biases created by unequal sampling effort in space and time [87,90,91].”
What ' s on the horizon for macroecology? (2012, background): “Spatially and taxonomically biased undersampling has been pointed out (Graham et al. 2004, Rondinini et al. 2006, Boakes et al. 2010), but this can be only the beginning of tackling the problem.”
Statistical solutions for error and bias in global citizen science datasets (2014, background): “…of a process that is auto-257 correlated in space or time (i.e., closely spaced observations are more alike 258 than more distant observations) can introduce bias, as eventual 259 understanding of the underlying process is dominated by information from the 260 clustered areas (Boakes et al., 2010).”
Mapping ignorance: 300 years of collecting flowering plants in Africa (2016, background): “However, the magnitude of the errors is rarely quantified (Hopkins, 2007; Boakes et al., 2010).”
sPlot – A new tool for global vegetation analyses (2019, background): “…have been derived from occurrence data (i.e., presenceonly data), and after being aggregated spatially, have a relatively coarse- grain scale (e.g., one- degree grid cells) without information on species co- occurrence at the meaningful scale of local communities (Boakes et al., 2010).”
Distributional migrations, expansions, and contractions of tropical plant species as revealed in dated herbarium records (2012, background): “The strong influence of collection biases is a noteworthy result in itself in that it highlights the importance of controlling for biases in studies using natural history collections (Schulman et al., 2007; Boakes et al., 2010) and illustrates that extreme care needs to be taken when interpreting ch”
Updated distribution and biogeography of amphibians and reptiles of Europe (2014, background): “…or Tel-mee (www.telmee.nl), the feedback of the centrally collected data into the national databases, and especially, the review and scientiﬁc validation process before the contributed data are included in these databases (Boakes et al., 2010; Bonter and Cooper, 2012; Ficetola et al., 2013).”
Handling missing values in trait data (2020, background): “This bias type was not tested by Penone et al. (2014), but it is likely to be common in ecology and evolution, because both trait databases (González-Suárez et al., 2012) and response values are biased (Boakes et al., 2010; Troudet et al., 2017).”
Accounting for spatially biased sampling effort in presence‐only species distribution modelling (2015, background): “…species distribution models and subsequent applications (Dennis & Thomas, 2000; Reddy & D avalos, 2003; Ara ujo & Guisan, 2006; Hortal et al., 2008; Boakes et al., 2010; Peterson et al., 2011), only more recently have solutions been explored for identifying (e.g. herbarium bias; Loiselle et al.,…”
Unstructured citizen science data fail to detect long‐term population declines of common birds in Denmark (2016, background): “…collected in an unstructured manner usually do not represent random samples and exhibit considerable spatial bias towards more densely populated regions (Boakes et al., 2010; Lin et al., 2015), protected areas, and areas rich in biodiversity and threatened species (Tulloch et al., 2013a).”
The strong influence of collection bias on biodiversity knowledge shortfalls of Brazilian terrestrial biodiversity (2016, background): “Moreover, possibly the most pervasive documented sampling bias in biodiversity is the concentration of specimen records near access routes, such as roads and navigable rivers and major cities (Kadmon et al., 2004; Moerman & Estabrook, 2006; Boakes et al., 2010; Vale & Jenkins, 2012; Sousa-Baena et a”