10.5061/DRYAD.S1660
Westgate, Martin J.
Australian National University
Tulloch, Ayesha I. T.
Australian National University
Barton, Philip S.
Australian National University
Pierson, Jennifer C.
Australian National University
Lindenmayer, David B.
Australian National University
Data from: Optimal taxonomic groups for biodiversity assessment: a
meta-analytic approach
Dryad
dataset
2016
surrogates and indicators
assemblage structure
cross-taxon congruence
2016-03-09T18:39:08Z
2016-03-09T18:39:08Z
en
https://doi.org/10.1111/ecog.02318
278446 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
A fundamental decision in biodiversity assessment is the selection of one
or more study taxa, a choice that is often made using qualitative criteria
such as historical precedent, ease of detection, or available technical or
taxonomic expertise. A more robust approach would involve selecting taxa
based on the a priori expectation that they will provide the best possible
information on unmeasured groups, but data to inform such hypotheses are
often lacking. Using a global meta-analysis, we quantified the proportion
of variability that each of 12 taxonomic groups (at the Order level or
above) explained in the richness or composition of other taxa. We then
applied optimization to matrices of pairwise congruency to identify the
best set of complementary surrogate groups. We found that no single taxon
was an optimal surrogate for both the richness and composition of
unmeasured taxa if we used simple methods to aggregate congruence data
between studies. In contrast, statistical methods that accounted for
well-known drivers of cross-taxon congruence (spatial extent, grain size,
and latitude) lead to the prioritization of similar surrogates for both
species richness and composition. Advanced statistical methods were also
more effective at describing known ecological relationships between taxa
than simple methods, and show that congruence is typically highest between
taxonomically and functionally dissimilar taxa. Birds and vascular plants
were most frequently selected by our algorithm as surrogates for other
taxonomic groups, but the extent to which any one taxon was the ‘optimal’
choice of surrogate for other biodiversity was highly context-dependent.
In the absence of other information – such as in data-poor areas of the
globe, and under limited budgets for monitoring or assessment – ecologists
can use our results to assess which taxa are most likely to reflect the
distribution of the richness or composition of ‘total’ biodiversity.
Data on cross-taxon congruence from the ecology literatureThe file
contains four tabs: 'articles' is a list of articles
investigated to identify suitable data; 'sites' contains
information on the spatial characteristics of the selected survey
locations; 'congruence' contains the raw data on cross-taxon
congruence extracted from each article; and 'taxa' gives
taxonomic information for each group listed in the
dataset.Westgate_etal_2016_Ecography_data.xlsx
global