Tuesday, February 28, 2012
Tuesday, February 14, 2012
A good null model is hard to find
Ecologists have always found the question of how communities assemble to be of great interest. However, studies of community assembly are often thwarted by the
large temporal and spatial scales over which processes occur,
making experimental tests of assembly theory difficult. As a result, researchers are often forced to rely
on observational data and make inferences about the mechanisms at play from
patterns alone. While historical assembly research focused on inferring evidence of competition or environmental filtering from patterns of species co-occurrence, more recent
research often looks at patterns of phylogenetic or trait similarity in a
community to answer these questions.
Not surprisingly, when methods rely heavily on observational data they are open to criticism: one of the most important outcomes of early community
assembly literature was the recognition that patterns that appeared to support
a hypothesis about competition or environmental filtering could in fact result by
random chance. This ultimately lead to the
widespread incorporation of null models, which are meant to simulate patterns
that might be observed by random chance (or other processes not under study),
against which the observed data can be compared. Patterns of functional and
phylogenetic information in communities can also be compared against null
expectations to ensure that patterns of phylogenetic or functional over- or under-dispersion can't arise due to chance alone. However, while
null models are an important tool in assembly research, they are sometimes as the foolproof solution to all of its problems.
In a new paper by Francesco de Bello, the author states
frankly “whilst reading null-model methods applied in the literature (indeed
including my work), one may have the impression of reading a book of magic
spells”. While null models are increasingly sophisticated, allowing researchers
to determine which processes to control for and which to leave out, de Bello
suggests that the decision to include or omit particular factors from a null
model can be unclear, making it difficult to interpret results or compare results across studies. Further, results from null models may not mean what researchers
expect them to mean.
Using the example of functional diversity (FD; variation in
trait values among species in a community), de Bellow illustrates how null models may
have different meanings than expected. Ideally, a null model for FD should produce
random values of FD, against which the observed values of FD can be compared. Interpreting the difference between the observed and random results can be done using the standardized effect size (SES, the standardized difference between the
observed and randomly generated FD values); SES values >0 show that traits are more
divergent than expected by chance, suggesting competition structures
communities. If SES<0, traits are more convergent than expected by chance,
suggesting environmental conditions structure communities. Finally, if SES ~0, then
trait values aren’t different from random. However, de Bello shows that the SES
is driven by the observed FD values, because the ‘random’ FD values are
dependent on the pool of observations sampled. This means that the values the
null model produces are ultimately dependent on those observed values, despite
the fact you plan to make inferences by comparing the null and observed values
as though they are independent. For example, consider the situation where you
are building a null model of community structure for plant communities found along
two vegetation belts. If the null model is constructed using all the plant
communities, regardless of the habitat they are found in, the resulting null FD
value will be higher, since species that are dissimilar and found in different
vegetation belts are being randomly selected as occurring in a community. If
null models are constructed separately for both vegetation belts, the null FD
value is lower, since species are more similar. The magnitude of the difference
between the null model and the observed values, and further, the biological
conclusions one would take from this study, would therefore depend on which null model was
constructed.
De Bello’s findings make important points about the
limitations of null models, particularly for functional diversity, but likely
for other types of response variable. The type of null model he explores is
relatively simplistic (reshuffling of species among sites), and the suggestion
that the species pool affects the null model is not unique (Shipley & Weiher, 1995).
However, even sophisticated and complex null models need to be biologically
relevant and interpretable, and null models are still frequently used incorrectly. Although only mentioned briefly, De Bello also
notes another problem with studies of community assembly, which is that popular
indices like FD, PD, and others may not always be able to distinguish correctly between
different assembly mechanisms (Mouchet et al. 2010, Mayfield & Levine, 2010), something that null
model do not control for.
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