Analysing botanical collecting effort in Amazonia and correcting for it in species range estimation

By L. Schulman, Tuuli Toivonen, Kalle Ruokolainen. 2007

199 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/41018ee064f2d51ff44b89ca1ea2c42622f6304e

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In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

Completeness of digital accessible knowledge of the plants of Brazil and priorities for survey and inventory (2014, background): “In Brazil, such studies have been carried out mainly in the Amazon (Hopkins, 2007; Schulman et al., 2007; Vale & Jenkins, 2012).”
How far can we go in simplifying biomonitoring assessments? An integrated analysis of taxonomic surrogacy, taxonomic sufficiency and numerical resolution in a megadiverse region (2012, background): “The huge diversity found in the Amazon would require an inordinately large number of taxonomists for a basic inventory of this diversity (Hopkins, 2007), and many tropical areas are still practically inaccessible, especially in the Amazon region (Schulman et al., 2007).”
A statistical explanation of MaxEnt for ecologists (2011, methodology): “, the specification of what constitutes a sample), or via sampling covariates in the model, provided survey details are available (Leathwick, 1998; MacKenzie & Royle, 2005; Schulman et al., 2007; Ward, 2007b).”
Predicting distributions of invasive species (2013, background): “Locational accuracy refers to whether the co-ordinates properly represent the sample to a precision relevant to the grain of the environmental data, while taxonomic accuracy refers to whether the record is truly for the species of interest (Anderson, 2012; Elith & Leathwick, 2009a Elith et al., 2013”
Accounting for spatially biased sampling effort in presence‐only species distribution modelling (2015, methodology): “Our method is similar to that presented by Schulman et al. (2007), who adjusted predicted values of species’ distributions to vary inversely with sampling intensity by down-weighting samples from locations of with higher sampling intensity.”
How Useful Are Species Distribution Models for Managing Biodiversity under Future Climates (2010, background): “Recurring spatial biases are characteristic of many biological survey data sets (see Frietag et al. 1998, Schulman et al. 2007), such as a tendency for higher sampling rates at accessible locations.”
Distributional migrations, expansions, and contractions of tropical plant species as revealed in dated herbarium records (2012, background): “…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 changes between historical and…”
Measuring and modelling biodiversity from space (2008, background): “Remote sensing may have a major role to play in helping to prioritize candidate locations for new reserves (Schulman et al., 2007a).”
Mapping ignorance: 300 years of collecting flowering plants in Africa (2016, background): “These findings are not unique in a global context: it is well known that botanical sampling in the Amazon is geographically associated with larger cities, major rivers, roads and proximity to research centres (Nelson et al., 1990; Hopkins 2007; Schulman et al., 2007; Engemann et al., 2015).”
Modelling the known and unknown plant biodiversity of the Amazon Basin (2007, background): “As Schulman et al. (2007) point out (see references therein), species distribution maps based on collecting data are very unreliable, as they tend to map the distribution of collection activity rather than the actual distribution of species.”
Partial versus Full Species Distribution Models (2012, background): “Second, even within the entire range of occurrence, collection localities tend to be biased to more accessible areas which can result in environmentally biased collections (Reddy & Davalos 2003; Hortal et al. 2007; Schulman et al. 2007).”
Keep collecting: accurate species distribution modelling requires more collections than previously thought (2011, background): “…at random but rather collections tend to be clumped around specific areas because of the nature of collecting campaigns and ease of access to sites within a particular habitat (Kadmon et al., 2004; Moerman & Estabrook, 2006; Schulman et al., 2007; Tobler et al., 2007; Loiselle et al., 2008).”
Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. (2009, methodology): “These occurrence data often exhibit strong spatial bias in survey effort (Dennis and Thomas 2000, Reddy and Dávalos 2003, Schulman et al. 2007), meaning simply that some sites are more likely to be surveyed than others; such bias is typically spatially autocorrelated, but this paper allows for…”