New methodology
This contribution derives from careful examination and analysis of the
growing suite of papers that analyze niche evolution across phylogenies
(e.g., Peterson et al. 1999;
Wiens and Graham 2005;
Knouft et al. 2006;
Losos 2008;
Evans et al. 2009;
Vieites et al. 2009;
Nyári and Reddy 2013;
Meseguer et al. 2015). Of concern is that
estimates of fundamental niches based on species’ realized niches are
not equivalent to their true fundamental niches, owing to constraints
imposed by the set of environments that can be observed within the area
accessible to a species (M ). The effect of limited available
environments in M lies in adding variation to niche estimates
that bias analyses of niche evolution toward concluding increased niche
lability. In other words, estimated fundamental niches include elements
of biogeographic limitations and environments associated with the
accessible areas of the different species. Indeed, use of summary
statistics to characterize species’ niches introduces further variation
related to the environmental vagaries of sampling, which has its own
intrinsic biases (Kadmon et al. 2004)
that are—again—reflected in the environmental signature of the
occurrence data that derive from the process.
Previous work has illustrated the precarious nature of correlational
ecological niche estimates in situations in which species’ responses to
environmental variables are not fully observable across M(Elith et al. 2011;
Owens et al. 2013; Araújo et al. 2013).
Such situations are, at best, ones in which modeling algorithms are
forced to extrapolate. At worst, these situations are ones in which
significant, predictive models are impossible to obtain
(Saupe et al. 2012;
Qiao et al. 2015). For analyses of niche
evolution in a phylogenetic context, however, environmentally-limitedM areas introduce consistent directional biases in the
analyses: estimated fundamental niches will vary more than does a
species’ true fundamental niche.
Analyzing ecological niche change on a phylogenetic tree without
considering uncertainty produces less-ambiguous conclusions (as
illustrated in Fig. 2), and certainly has the advantage of ease of
implementation (e.g., simply calculating the median or mean of
environmental values across all occurrences for a species). Our
explorations, presented here and previously
(Ribeiro et al. 2016;
Saupe et al. 2017), indicate that this
approach comes with a significant cost: niche change may be over- or
under-estimated, introducing substantial biases in reconstructing
evolutionary change in niches through time. The simple simulation
results reported herein mirror the results of more sophisticated
simulations that incorporate dispersal and temporal components
(Saupe et al. 2017). Our simulation and
empirical case further develops the body of evidence suggesting that
bin-based coding accounting for uncertainty explicitly is a valuable
methodological improvement. First, bin-based coding is free of the
assumption that more occurrences at a particular environmental variable
value indicate that that value is more suitable than another—indeed,
it may just be more common within a species’ M or more likely
to be sampled by researchers. Second, as we noted above in the Methods
section, σ2 describes degrees of change among traits
or groups (Revell et al. 2008;
Cooper et al. 2011); when these changes
are small, as in our simulation and for temperature in the orioles
example, traditional coding methods and our new bin-based method
(incorporating the known breadth of species’ niches), perform similarly.
However, when variation is high, such as for precipitation in the
orioles example, traditional coding methods appear to grossly inflate
estimates of the rate of niche evolution across the phylogeny.