468
This leaves it to the data user to work around and with the data issues. Means of
dealing with some of the aforementioned challenges regarding data availability and
quality are (i) gap filling of missing trait data (Swenson 2014; Schrodt et al. 2015)
and (ii) spatial extrapolation of plant traits (Butler et al. 2017), accounting for “dark
diversity,” i.e., the portion of species absent from species distribution data such as
biodiversity maps (Ronk et al. 2015). Care must be taken to avoid circularity (e.g.,
using phylogeny to gap fill trait data when including an aspect of taxonomy or phylogeny in subsequent analyses). In addition, a possible lack of representativeness
throughout analyses should be considering, as well as the fact that different
approaches might require different data collection protocols (e.g., statistical versus
process models).
17.4.2 In-Situ Abiotic Factors
Much abiotic information, including data from the lithosphere, atmosphere, hydrosphere, and cryosphere, can be assessed remotely (see Record et al., Chap. 10, in
this book for a thorough discussion, including access to climatological data).
However, many important aspects are only accessible from in-situ sources. These
include, for example, soil chemical and physical characteristics, geomorphology,
and subsurface hydrology (see Table 17.1). Many are available at static temporal but
relatively high spatial resolutions with high associated uncertainties in geolocation,
bias due to different sampling efforts depending on the location, etc. For example,
Generalized Linear Interactive Modelling (GLIM), a lithology and mineralogy data
source, has been shown to be highly biased by country boundaries—an issue that is
perpetuated in other products using GLIM, such as the SoilGrids database (Hengl
et al. 2017), resulting in error propagation to higher-level agglomerate analyses.
Other challenges include breakdown of concepts and assumptions related to up- and
downscaling of composite products (e.g., inter-cell redistribution of soil water at
fine spatial resolution, which can be ignored at coarser resolutions) and a lack of
knowledge about parameters and processes acting at different resolutions (Bierkens
et al. 2015).
17.4.3 Socioeconomic Factors and Land Use
Socioeconomic aspects are often ignored in assessments of plant biodiversity at the
landscape scale, despite the obvious imprint humans have left on most of the globe.
For example, Abelleira Martínez et al. (2016) found that studies linking local plant
trait measurements to environmental gradients without accounting for anthropogenic effects on these traits render them of limited use due to the multivariate nature
of the processes governing observed patterns. The same applies to studies integrating in-situ plant trait variability with land cover types, e.g., for ES assessments
F. Schrodt et al.
This leaves it to the data user to work around and with the data issues. Means of
dealing with some of the aforementioned challenges regarding data availability and
quality are (i) gap filling of missing trait data (Swenson 2014; Schrodt et al. 2015)
and (ii) spatial extrapolation of plant traits (Butler et al. 2017), accounting for “dark
diversity,” i.e., the portion of species absent from species distribution data such as
biodiversity maps (Ronk et al. 2015). Care must be taken to avoid circularity (e.g.,
using phylogeny to gap fill trait data when including an aspect of taxonomy or phylogeny in subsequent analyses). In addition, a possible lack of representativeness
throughout analyses should be considering, as well as the fact that different
approaches might require different data collection protocols (e.g., statistical versus
process models).
17.4.2 In-Situ Abiotic Factors
Much abiotic information, including data from the lithosphere, atmosphere, hydrosphere, and cryosphere, can be assessed remotely (see Record et al., Chap. 10, in
this book for a thorough discussion, including access to climatological data).
However, many important aspects are only accessible from in-situ sources. These
include, for example, soil chemical and physical characteristics, geomorphology,
and subsurface hydrology (see Table 17.1). Many are available at static temporal but
relatively high spatial resolutions with high associated uncertainties in geolocation,
bias due to different sampling efforts depending on the location, etc. For example,
Generalized Linear Interactive Modelling (GLIM), a lithology and mineralogy data
source, has been shown to be highly biased by country boundaries—an issue that is
perpetuated in other products using GLIM, such as the SoilGrids database (Hengl
et al. 2017), resulting in error propagation to higher-level agglomerate analyses.
Other challenges include breakdown of concepts and assumptions related to up- and
downscaling of composite products (e.g., inter-cell redistribution of soil water at
fine spatial resolution, which can be ignored at coarser resolutions) and a lack of
knowledge about parameters and processes acting at different resolutions (Bierkens
et al. 2015).
17.4.3 Socioeconomic Factors and Land Use
Socioeconomic aspects are often ignored in assessments of plant biodiversity at the
landscape scale, despite the obvious imprint humans have left on most of the globe.
For example, Abelleira Martínez et al. (2016) found that studies linking local plant
trait measurements to environmental gradients without accounting for anthropogenic effects on these traits render them of limited use due to the multivariate nature
of the processes governing observed patterns. The same applies to studies integrating in-situ plant trait variability with land cover types, e.g., for ES assessments
F. Schrodt et al.