Because GLS and BAR reconstructions generate quite-different outputs, direct comparisons are not possible; however, qualitatively, single-value reconstructions appear to evolve much more quickly than bin-based reconstructions, especially near the base of the tree and in groups with a preponderance of island endemics. GLS reconstructions based on median environmental values fell within ranges of values inferred using the bin-based method at every node for both environmental variables (Supplementary Tables 3 and 5). However, these reconstructions were only congruent with maximum parsimony BAR reconstructions for 24 of 33 nodes for mean annual temperature, and for none of the nodes for annual precipitation (Supplementary Tables 2 and 4).
 

Discussion

New methodology
            This contribution derives from careful examination and analysis of the growing suite of papers analyzing 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). It is likely that fundamental niches and realized niches are rarely equivalent, owing to constraints imposed by the set of environments that can be observed within areas accessible to a species (M) (Soberón and Peterson, 2011). The limited environments present in areas accessible to species typically will add variation to niche estimates that will bias analyses of niche evolution toward concluding increased niche lability (Ribeiro et al. 2016; Saupe et al. 2017). In addition, 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 (Saupe et al. 2017).
            Analyzing ecological niche change on a phylogenetic tree without considering uncertainty produces more concise conclusions and is easier to implement (e.g., calculating the median or mean of environmental values across all occurrences for a species and performing a single reconstruction calculation). However, previous studies indicate that this approach comes with a cost: niche change may be over- or under-estimated, introducing biases in reconstructing evolutionary change in niches through time (Ribeiro et al. 2016; Saupe et al. 2017). Our empirical example using orioles shows patterns that are qualitatively consistent with these findings: GLS reconstructions of ancestral node characteristics varied more near the base of the tree and in clades dominated by narrow-range endemic species with incompletely-characterized abiotic ecological niches than in clades with fewer narrow-range endemics.
Admittedly, we currently lack a method for quantitative assessment of niche evolution rates estimated from BAR reconstructions that is comparable to rates calculated for single continuous value reconstructions (especially in light of differences in how evolutionary models are applied and how rates are estimated for continuous and discrete characters). However, our BAR reconstructions appear to be qualitatively more robust to noise in the data introduced by narrow-range endemics that are incompletely characterized, as it recovered conserved ranges of suitable habitat for all basal Icterus lineages (except for parsimony reconstructions of precipitation). Furthermore, BR coding less likely to be skewed by instances of biased sampling, (i.e. a greater frequency of occurrences within a particular environmental range can skew niche estimates based on summary statistics). Indeed, a greater abundance of occurrences in particular environments may not be due to those environments being more suitable to a species than another suite of environments, but merely that those environments are more common within a species’ M or more likely to be sampled by researchers. This is illustrated by our simulation reconstruction, in which the median ancestral temperature for the cool-niche simulated species was inferred to be warmer owing to biased tip state characterizations.
 
Oriole niche evolution
The genus Icterus exists in many different environments, which suggests that the niches of these species have diversified. Indeed, when we look at patterns of niche evolution inferred using GLS, we found frequent apparent niche shifts across the phylogeny, particularly within clades dominated by island endemics (Annotated Code Supplement 4, Supplementary Tables 2-5). However, BAR reconstructions found little evidence of change in the inferred fundamental ecological niche across the phylogenetic history of the genus, particularly when reconstructions were done using the maximum likelihood algorithm. This pattern is consistent with the fact that species of Icterus that “left” the Tropics (i.e. migratory species) move into northern areas of North America in the breeding season only—a special case of niche conservatism termed “niche following” in previous work (Joseph and Stockwell 2000; Nakazawa et al. 2004).
The overall tendency across the history of the genus Icterus was one of remarkable niche stability, notwithstanding the GLS results. Particularly invariant was the upper end of the temperature tolerance spectrum (Fig. 3; Supplementary Fig. 2, Tables 2 and 3; Annotated Code Supplement 4). This observation coincides with recent results from Araújo et al. (2013), who presented a meta-analysis that concluded that heat tolerance was much more constrained over evolutionary history than cold tolerance. Importantly, though, our proposed framework for characterizing ecological niches and subsequent ancestral niche inference may underestimate true amounts of niche evolution because the method only concludes niche change when explicit evidence exists, which we consider to be a desirable quality. Still, further detailed simulation study is needed to examine fully the sensitivity of our proposed methods to true niche evolution in the face of various biasing or obfuscating factors.
Focusing on maximum likelihood BAR reconstructions, which showed clearer patterns with less uncertainty than parsimony BAR reconstructions, we identified niche reductions for species that are relative habitat specialists within Icterus. Icterus orioles are a predominately lowland group, although some species occur in foothills and low montane regions adjacent their core lowland ranges. We identified reductions in high temperature tolerance for two species that specialize in Mesoamerican montane habitats, I. abeillei and I. maculialatus, and reductions in low temperature tolerance for two strictly lowland tropical species, I. fuertesi and I. chrysocephalus. Icterus orioles occupy a variety of forest types across a variety of precipitation regimes. However, for two species that specialize in dry forest, I. auratus of the Yucatán Peninsula and I. graceannae of the Tubezian region, we identified suitable niches corresponding to reduced precipitation.
 

Conclusion

The challenge of understanding change in species’ ecological niches aceoss evolutionary history lies in characterizing the entirety of a species’ niche. We present a simple methodology that directly incorporates knowledge gaps based on incomplete niche characterization. We see a number of next steps in developing this methodology further—specifically, developing nichevol tools to encompass Bayesian estimation approaches and considering alternative evolutionary models. We would also take into account the frequency of occurrence of environmental conditions across the accessible area of each species in making conclusions about niche limitations (e.g. Meyer and Pie 2018)—that is, non-occurrence in relatively rare environments should perhaps not be taken as evidence of niche limitation. Finally, we plan to develop a method for estimating the likely range of niche evolution rates encompassing uncertainty using our bin-based method. We are exploring implementation of these next steps in coming applications of this methodology.
 

Data Accessibility Statement

Analysis scripts, annotate HTML script reports, M polygons for both the virtual species and empirical case, final oriole occurrence datasets, and results of niche characterization are accessible via Dryad (DOI: https://doi.org/10.5061/dryad.c866t1g3j).
 

Competing Interests Statement

The authors have no competing interests.
 

Author Contributions

All authors contributed to the conceptualization, theory, and design of the study, and discussion of results, led by HLO. HLO, EES, ATP and VR wrote the manuscript with input from all authors. PAH, JCC, HLO, EES, and ATP prepared data for the empirical section of the study. HLO, VR, MEC, VB, NB, and ATP performed elements of analysis. MEC aided HLO with re-analysis of the original study, and wrote the nichevol R package.
 

Acknowledgments

We thank the members of the University of Kansas Ecological Niche Modeling Group for their support, assistance, and advice, during the development of this contribution. Luis Escobar provided help with NicheA, which was central to early explorations during this project. We also thank Richard Glor, Mark Holder, Daniel Reuman, and Jorge Soberón (University of Kansas) for their time and advice regarding complex analytical challenges.
 

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