As such, any characterization of fundamental ecological niches that relies on inference from species’ geographic distributions (i.e. realized niche) will be incomplete (Saupe et al. 2012; Owens et al. 2013). A species’ fundamental niche becomes particularly difficult to approximate from its realized niche when its geographic range approaches the limits of the area to which it can disperse (as may be the case for many island endemic species; Saupe et al. 2012). Hence, although the estimated niche of a lineage through time may show variation in response to inherited adaptations that alters the lineage’s fundamental niche, that variation may also derive from changes in the set of environments accessible to that lineage, which do not represent a genetically-inherited set of adaptations or changes in the fundamental ecological niche (Araújo et al. 2013).
Methodologies that use estimates based on species’ realized niches to characterize ecological niches in phylogenetic analyses are known to overestimate true amounts of niche change (Saupe et al. 2017). Here, we present a new framework to characterize species’ niches, which incorporates consideration of areas accessible to the species over relevant time periods (referred to as M; Soberón and Peterson, 2005; Phillips et al. 2009; VanDerWal et al. 2009; Barve et al. 2011;). Estimating and accounting for this accessible region has been recognized as important when generating niche or distribution models that use background or pseudo-absence data for calibration (Phillips et al. 2009; Elith et al. 2010; Barve et al. 2011). If regions accessible to a species are ignored when selecting the geographic extent for model calibration, fitted models may erroneously estimate suitable niche conditions. However, even niche estimates derived from presence data (i.e., without a modelling component) should consider M, as doing so provides one of the only ways to assess in which cases niche estimates are likely to be truncated. Specifically, when environments across M do not encompass conditions beyond those under which the species in question is observed, no evidence is available regarding the environmental limits of the species (Fig. 1b).
Our new binned-range (BR) character-coding method decomposes the broader environment occupied by and accessible to a clade into discrete bins, and scores each bin as suitable, unsuitable, or uncertain for a given species (Fig. 1b), thereby accounting for potential cases of incomplete niche characterization. We illustrate the utility of summarizing species’ niches in this way via simulation that compares ancestral niche reconstructions based on BR coding (Binned Ancestral Range; BAR) to those estimated using a more traditional analysis (generalized least-squares reconstructions based on the median suitable value of a variable for each species based on its realized niche). We demonstrate the utility of our approach with an empirical example, inferring patterns of ecological niche evolution in New World orioles (Icterus spp.; see, e.g., Fig. 2). This empirical example highlights the utility of BAR reconstructions in terms of incorporating uncertainty explicitly and considering species’ ecological niches as a ranged response instead of as a single value.