Biodiversity informatics: managing and applying primary biodiversity data.

By Jorge Soberón, Townsend Peterson. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2004

433 citations recorded by the source corpus.

Subfields: Ecological Modeling, Genetics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/702f7051ebc6bb9c1a6005b8115dc7b74bfada68

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Citations in the corpus

Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling (2009, methodology): “While the value of publicly available sample locality data is not questioned, the consequent introduction of errors in the accuracy of specimen identity and georeferencing could be problematic for developing ENMs from public data sources (Graham et al., 2004; Soberón & Peterson, 2004).”
Not as good as they seem: the importance of concepts in species distribution modelling (2008, background): “On one hand, a huge amount of distributional data are being gathered in biodiversity data bases and made freely available through the internet (see Soberón & Peterson, 2004 and http://www.gbif.org/).”
KnowBR: An application to map the geographical variation of survey effort and identify well-surveyed areas from biodiversity databases (2018, positive): “Current development of information technology and biodiversity informatics allows storing, retrieving, sharing, filtering and manipulating massive datasets such as those on species distributions (Bisby, 2000; Godfray, 2002; Soberón and Peterson, 2004; Graham et al., 2004; Guralnick et al., 2007).”
Effects of the number of presences on reliability and stability of MARS species distribution models: the importance of regional niche variation and ecological heterogeneity (2010, background): “This implies that the current trend of decreasing the number of collecting expeditions should be reversed, as well as continuing to create or reinforce existing biodiversity information systems (Bisby 2000; Soberon & Peterson 2004; Guralnick et al. 2007).”
The uncertain nature of absences and their importance in species distribution modelling (2010, background): “…of distributional information, which is frequently (if not always) incomplete and biased (see Gaston 1991, Gaston and Blackburn 1994, Dennis et al. 1999, Dennis and Thomas 2000, Graham et al. 2004, Soberón and Peterson 2004, Hortal et al. 2007, 2008, Lobo et al. 2007 and references therein).”
Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent (2011, background): “Museum and herbarium data typically derive from any individual surveys conducted across several decades by umerous researchers sampling with different techniques, intenity, and periods of time (Soberón and Peterson, 2004).”
openModeller: a generic approach to species’ potential distribution modelling (2011, methodology): “With the growth of biodiversity Web Portals and the establishment of protocols and data standards for biodiversity data [2, 43–45], two other drivers were implemented to retrieve occurrence data from the following remote data sources: GBIF’s database (through GBIF’s occurrence record data service) a”
Taxonomist survey biases and the unveiling of biodiversity patterns (2009, background): “…that taxonomists tend to survey repeatedly in some localities and landscape types (Dennis and Hardy, 1999; Funk and Richardson, 2002; Reddy and Dávalos, 2003; Soberón and Peterson, 2004; Diniz-Filho et al., 2005; Moerman and Esta- brook, 2006; Pautasso and McKinney, 2007; Schulman et al., 2007).”
Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? (2007, background): “…museums contain a vast storehouse of valuable information on biodiversity that offers great potential to advance conservation planning and decision-making (cf. Graham et al., 2004; Soberón & Peterson, 2004), the information for many species is probably still insufficient for indiscriminate use.”
Model‐based uncertainty in species range prediction (2006, background): “Further uncertainties result from potential inaccuracies in the available species distribution data sets (Griffiths et al., 1999; Soberón & Peterson, 2004) and, perhaps most fundamentally in the case of climate change applications, uncertainties are inherent in predicted scenarios of future…”
Sampling biases shape our view of the natural world (2020, background): “Many data exist which have essentially ‘leaked’ out of the mobilization pipeline, such as at the stage of georeferencing, and this is a major challenge to leveraging databased specimen records (Soberón and Peterson 2004, Peterson et al. 2018).”
Bias in freshwater biodiversity sampling: the case of Iberian water beetles (2008, background): “…with other taxa and territories (Dennis et al ., 1999; Dennis & Thomas, 2000; Zaniewski et al ., 2002; Reutter et al ., 2003; Graham et al ., 2004; Soberón & Peterson, 2004; Mart’nez-Meyer, 2005; Romo et al ., 2006; Hortal et al ., 2007; Lobo et al ., 2007; Soberón et al ., 2007), the available…”