On the Inclusion of Adaptive Potential in Species Distribution Models: Towards a Genomic-Informed Approach to Forest Management and Conservation

Abstract

Ecological modeling refers to the construction and analysis of mathematical models aimed at understanding the complexity of ecological processes and at predicting how real ecosystems might evolve. It is a quickly expanding approach boosted by impressive accelerations in the availability of computational resources and environmental databases. In the light of foreseeing the effect of climate change on forest ecosystems, the branch of ecological modeling focusing on species distribution models (SDMs) has become widely used to estimate indices of habitat suitability and to forecast future tree distributions. However, SDMs are usually informed based solely on environmental data without any reference to the genetic makeup underlying responses to the environment, the possibility of exchanging variants helping to persist in situ, or the capacity to chase suitable conditions elsewhere. Among the main evolutionary processes that may complement forecasts of range shifts are local adaptation and gene flow, i.e., the occurrence of genetic variants conferring a population the optimal fitness in its own habitat and the exchange of adaptive alleles between populations. Local adaptation and gene flow could be described by indices of genetic diversity and structure, genetic load, genomic offset, and an admixture of genetic lineages. Here, we advocate for the development of a new analytical approach integrating environmental and genomic information when projecting tree distributions across space and time. To this aim, we first provide a literature review on the use of genetics when modeling intraspecific responses to the environment, and we then discuss the potential improvements and drawbacks deriving from the inclusion of genomic data into the current SDM framework. Finally, we speculate about the potential impacts of genomic-informed predictions in the context of forest conservation and provide a synthetic framework for developing future forest management strategies.

The latitudinal trend in genetic diversity and distinctiveness ofQuercus roburrear edge forest remnants calls for new conservation priorities

“…A negative relationship between Ne and the level of correlated paternity was observed in Q. robur (Vranckx et al 2014), suggesting that inbreeding might increase in the future as the probability of mating among half- and full-sibs increases too. Monitoring natural regeneration is crucial to investigate how neutral and adaptive genetic variation is transmitted through generations (Lowe et al 2015; Vajana et al 2023). The low to moderate Ne values of our populations might predict a significant depauperization in the juvenile cohorts.…”

ClimateDT: A Global Scale-Free Dynamic Downscaling Portal for Historic and Future Climate Data

“…One of the main outputs of ecological models such as Species Distribution Models [72], Joint Species Distribution Models [73,74], reaction norms [28,30], response functions [71,[75][76][77], and transfer models [27,78] is the prediction of the possible changes in the spatial distribution and/or the performance of living organisms under changing climatic regimes. An additional feature is to detect provenances that may show better adaptation to the future climate predicted in a specific area [79][80][81]. Such models may be used as valid tools to support assisted gene-flow and assisted-migration strategies, but their performance is highly dependent on the quality of the climatic information used to drive the model and to make predictions [81,82].…”

“…An additional feature is to detect provenances that may show better adaptation to the future climate predicted in a specific area [79][80][81]. Such models may be used as valid tools to support assisted gene-flow and assisted-migration strategies, but their performance is highly dependent on the quality of the climatic information used to drive the model and to make predictions [81,82]. The higher the uncertainty in the modeling steps, the more uncertain and biased the predictions could be.…”