Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree

Abstract

Identifying and predicting how species ranges will shift in response to climate change is paramount for conservation and restoration. Ecological niche models are the most common method used to estimate potential distributions of species; however, they traditionally omit knowledge of intraspecific variation that can allow populations to respond uniquely to change. Here, we aim to test how population X environment relationships influence predicted suitable geographic distributions by comparing aggregated population-level models with species-level model predictions of suitable habitat within population ranges and across the species’ range. We also test the effect of two variable selection methods on these predictions–both addressing the possibility of local adaptation: Models were built with (a) a common set, and number, of predictors and, (b) a unique combination and number of predictors specific to each group’s training extent. Our study addresses the overarching hypothesis that populations have unique environmental niches, and specifically that (1) species-level models predict more suitable habitat within the ranges of genetic populations than individual models built from those groups, particularly when compared models are built with the same set of environmental predictors; and (2) aggregated genetic population models predict more suitable habitat across the species’ range than the species-level model, an = d this difference will increase when models are trained with individualized predictors. We found the species models predicted more habitat within population ranges for two of three genetic groups regardless of variable selection, and that aggregated population models predicted more habitat than species’ models, but that individualized predictors increased this difference. Our study emphasizes the extent to which changes to model predictions depend on the inclusion of genetic information and on the type and selection of predictors. Results from these modeling decisions can have broad implications for predicting population-level ecological and evolutionary responses to climate change.

Stacked distribution models predict climate-driven loss of variation in leaf phenology at continental scales

“…All variables were the same spatial resolution (30 arc second; ~1 km at the Equator) and were projected into the same coordinate system (WGS1984) in ArcMap 59 . Based on a previous set of species distribution models built to describe the species’ range 60 , we selected a subset of the 27 available climatic variables. This is based on common practice of using expert judgment on the ecology of the taxa 61 to reduce multi-collinearity and over-fitting of models 62 .…”

The eco‐evolutionary role of fire in shaping terrestrial ecosystems

“…Secondarily, it is also important to identify many of the barriers to broad inference in ecology and evolution. It is increasingly clear that integrating hierarchical genetic structure or phylogenetic structure is critical to the accuracy of results and scope of conclusions one can draw in most systems (Bayliss, Papeş, et al, 2022; Bayliss, Mueller, et al, 2022; Love et al, 2023; Read et al, 2016). Finally, it is important to develop new experimental frameworks that limit context‐dependent outcomes (Catford et al, 2021) and can be used to understand geographic variation in species interactions and feedbacks that drive ecosystem function (Van Nuland et al, 2016; Ware et al, 2019), as they related to fire disturbances.…”

Identifying ecological and evolutionary research targets and risks in climate change studies to break barriers to broad inference

“…Over the last 20 years [55,56] it has become apparent that genetic structure needs to be incorporated in studies that estimate organismal responses to climate change to account for the influence of individual variation on the patterns and drivers of evolution and to control for non-independence among individuals and species. Over time, evidence demonstrating the critical importance of genetic variation in establishing broad inference across levels of organization has continued to grow [53,54]. However, the omission of data related to genetic structure is still common.…”

“…Phenotypic variation among species, or populations of a species, drives organismal responses to climate change [57]. Recent studies have clearly shown that the climatic drivers of species ranges and phenotypic variation vary depending upon whether or not genetic structure was incorporated into statistical models (for example, see bud break phenology in Populus species [54,58], and distribution and survival models in Pinus species [59]). More importantly, when species-level models were subsequently applied to populations within species, predictions of species responses to climate change varied by up to 50% compared to population specific models [59].…”

“…Consideration to broaden inference: To what extent do experimental approaches incorporate multiple global change factors and represent realistic future scenarios? Risk if excluded: Unrealistic treatments and outcomes over limited geographic or temporal range could produce irrelevant answers to contemporary questions.While comprehensively addressing each of these questions and limitations is difficult, we must overcome these challenges to reduce bias in the data, accurately estimate effect sizes, and increase the power and reliability of organismal response to climate change forecasts[53,54].…”