463
methods and data products will be applicable to continental and global scales, too.
Where the RS data products are described in other chapters, we only report on nonremotely sensed products; otherwise, references to both are given. Details of data,
spatial, and temporal resolution as well as the formats available are given in
Table 17.1.
17.4.1 In-Situ Biodiversity-Related Data
Decades of field work by dedicated researchers, their assistants, and students have
resulted in collection of large amounts of biodiversity data, including plant species
records on geographical location and abundance, traits of individual species, taxonomy, and phylogenetic data, as well as information on associated parameters such
as pollination or dispersal and growth. This plethora of information is often scattered, hidden in scientific publications and a range of online (and offline) databases,
herbaria, and agency reports. One means of retrieving relevant data semiautomatically from online sources is web scraping. Tools have been developed that make this
a viable option for people who are not experts in languages routinely used for creating web pages and applications. These include several R packages (e.g., rvest, xml2,
httr, TR8), Python libraries (e.g., Beautiful Soup), online tools (e.g., Nokogiri), and
software assisting with identifying relevant CSS selectors on websites (e.g., Selector
Gadget).
Recent efforts to cover this step and make dissemination of data more traceable,
convenient, and standardized have resulted in large databases covering all the aspects
discussed above. For example, large global databases exist for plant functional traits
(e.g., TRY, Kattge et al. 2011), plant community data (species co- occurrences; sPlot,
Dengler and sPlot Core Team 2014), plant phylogeny (e.g., TreeBASE, Smith and
Brown 2018; Open Tree of Life), species distributions (e.g., Global Biodiversity
Information Facility (GBIF), and botanical description and identification tools
(e.g., JSTOR’s Global Plants) to name just a few.
Some issues with using such large databases, however, are unavoidable.
Regarding plant phylogeny data, one needs to be aware of the lack of molecular data
associated with most species of plants resulting in many phylogenetic placements
being based on data at the genus or even family level (Smith and Brown 2018).
Where genetic data are available at the species level, large uncertainties with regard
to the placement of many taxa remain (Smith and Brown 2018), and, increasingly,
genetic sequences have not yet been linked to species names (so-called dark taxa).
Species distribution data, on the other hand, are known to have an inherently large
spatial sampling bias (see, e.g., Fig. 17.2) and generally lack absence data, which
can inflate the effect of sampling bias even further (Barbet-Massin et al. 2012;
Kramer-Schadt et al. 2013; Beck et al. 2014; Maldonado et al. 2015).
On the other hand, in the case of trait data, for example, TRY—a global database
of plant functional traits—has been shown to be biased toward more extreme trait
values, that is, frequently measured species consistently have higher or lower trait
17 Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study…
methods and data products will be applicable to continental and global scales, too.
Where the RS data products are described in other chapters, we only report on nonremotely sensed products; otherwise, references to both are given. Details of data,
spatial, and temporal resolution as well as the formats available are given in
Table 17.1.
17.4.1 In-Situ Biodiversity-Related Data
Decades of field work by dedicated researchers, their assistants, and students have
resulted in collection of large amounts of biodiversity data, including plant species
records on geographical location and abundance, traits of individual species, taxonomy, and phylogenetic data, as well as information on associated parameters such
as pollination or dispersal and growth. This plethora of information is often scattered, hidden in scientific publications and a range of online (and offline) databases,
herbaria, and agency reports. One means of retrieving relevant data semiautomatically from online sources is web scraping. Tools have been developed that make this
a viable option for people who are not experts in languages routinely used for creating web pages and applications. These include several R packages (e.g., rvest, xml2,
httr, TR8), Python libraries (e.g., Beautiful Soup), online tools (e.g., Nokogiri), and
software assisting with identifying relevant CSS selectors on websites (e.g., Selector
Gadget).
Recent efforts to cover this step and make dissemination of data more traceable,
convenient, and standardized have resulted in large databases covering all the aspects
discussed above. For example, large global databases exist for plant functional traits
(e.g., TRY, Kattge et al. 2011), plant community data (species co- occurrences; sPlot,
Dengler and sPlot Core Team 2014), plant phylogeny (e.g., TreeBASE, Smith and
Brown 2018; Open Tree of Life), species distributions (e.g., Global Biodiversity
Information Facility (GBIF), and botanical description and identification tools
(e.g., JSTOR’s Global Plants) to name just a few.
Some issues with using such large databases, however, are unavoidable.
Regarding plant phylogeny data, one needs to be aware of the lack of molecular data
associated with most species of plants resulting in many phylogenetic placements
being based on data at the genus or even family level (Smith and Brown 2018).
Where genetic data are available at the species level, large uncertainties with regard
to the placement of many taxa remain (Smith and Brown 2018), and, increasingly,
genetic sequences have not yet been linked to species names (so-called dark taxa).
Species distribution data, on the other hand, are known to have an inherently large
spatial sampling bias (see, e.g., Fig. 17.2) and generally lack absence data, which
can inflate the effect of sampling bias even further (Barbet-Massin et al. 2012;
Kramer-Schadt et al. 2013; Beck et al. 2014; Maldonado et al. 2015).
On the other hand, in the case of trait data, for example, TRY—a global database
of plant functional traits—has been shown to be biased toward more extreme trait
values, that is, frequently measured species consistently have higher or lower trait
17 Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study…