Integrating genomics in population models to forecast translocation success

Abstract

Whole-genome sequencing is revolutionizing our understanding of organismal biology, including adaptations likely to influence demographic performance in different environments. Excitement over the potential of genomics to inform population dynamics has prompted multiple conservation applications, including genomics-based decision-making for translocation efforts. Despite interest in applying genomics to improve translocations, there is a critical research gap: we lack an understanding of how genomic differences translate into population dynamics in the real world. We review how genomics and genetics data could be used to inform organismal performance, including examples of how adaptive and neutral loci have been quantified in a translocation context, and future applications. Next, we discuss three main drivers of population dynamics: demographic structure, spatial barriers to movement, and introgression, and their consequences for translocations informed by genomic data. Finally, we provide a practical guide to different types of models, including size-structured and spatial models, that could be modified to include genomics data. We then propose a framework to improve translocation success by repeatedly developing, selecting, and validating forecasting models. By integrating lab-based and field-collected data with model-driven research, our iterative framework could address long-standing challenges in restoration ecology, such as when selecting locally adapted genotypes will aid translocation of plants and animals.

Conservation genomics of an endangered arboreal mammal following the 2019–2020 Australian megafire

“…We provide crucial baseline genetic data for the species prior to the genetic impacts from this natural disaster. Such information is essential to effectively monitor and inform species recovery 111 , 112 . We provide a detailed list of actions required for each location within our study (Supplementary 4 ), but also recognize that pragmatism is required when prioritizing expenditure of often limited funding available 51 .…”

Expanding the conservation genomics toolbox: Incorporating structural variants to enhance genomic studies for species of conservation concern

“…Regardless, as we learn more about the mechanisms underlying fitness traits, especially traits associated with negative fitness, there is a pressing need to establish comprehensive evidence‐based management strategies that mitigate the impact of these traits while minimizing the loss of genome‐wide diversity. Where we see the most promise for this is the emerging practice of integrating WGS data into individual‐based models, which will enable different management actions to be modelled and compared (Seaborn et al, 2021 ). Such models will allow researchers to forecast the consequences of selection‐based management of fitness traits underpinned by adaptive or deleterious SVs on genome‐wide diversity, thus better informing conservation management actions.…”

“…Where we see the most promise for this is the emerging practice of integrating WGS data into individual-based models, which will enable different management actions to be modelled and compared (Seaborn et al, 2021). Such models will allow researchers to forecast the consequences of selection-based management of fitness traits underpinned by adaptive or deleterious SVs on genome-wide diversity, thus better informing conservation management actions.…”

Weaving place‐based knowledge for culturally significant species in the age of genomics: Looking to the past to navigate the future

“…However, testing relationships between genetic variation and fitness across time and complex spatial landscapes is challenging, especially for widely distributed, non-model animal species (e.g., Liddell et al, 2020;Seaborn et al, 2021). These relationships are further complicated by diversity in genomic architecture (e.g., in copy number variation, chromosome inversions, and transposable elements; Dorant et al, 2020;Wellenreuther et al, 2019;Wold et al, 2021) and by sources of adaptive potential that extend into the realm of transcriptomics (e.g., Oostra et al, 2018).…”

“…For example, increased research capacity and capability to characterize genomic markers (e.g., single‐nucleotide polymorphisms, SNPs) under selection ( adaptive variation ) is informing how populations are prioritized for conservation (e.g., Barbosa et al, 2018; Funk et al, 2012; Harrisson et al, 2017), including decisions around whether or how to translocate (e.g., Capel et al, 2021; Furlan et al, 2020; MacLachlan et al, 2021; Robinson et al, 2021). However, testing relationships between genetic variation and fitness across time and complex spatial landscapes is challenging, especially for widely distributed, non‐model animal species (e.g., Liddell et al, 2020; Seaborn et al, 2021). These relationships are further complicated by diversity in genomic architecture (e.g., in copy number variation, chromosome inversions, and transposable elements; Dorant et al, 2020; Wellenreuther et al, 2019; Wold et al, 2021) and by sources of adaptive potential that extend into the realm of transcriptomics (e.g., Oostra et al, 2018).…”