Individual-based landscape genomics for conservation: An analysis pipeline

Abstract

Landscape genomics can harness environmental and genetic data to inform conservation decisions by providing essential insights into how landscapes shape biodiversity. The massive increase in genetic data afforded by the genomic era provides exceptional resolution for answering critical conservation genetics questions. The accessibility of genomic data for non-model systems has also enabled a shift away from population-based sampling to individual-based sampling, which now provides accurate and robust estimates of genetic variation that can be used to examine the spatial structure of genomic diversity, population connectivity and the nature of environmental adaptation. Nevertheless, the adoption of individual-based sampling in conservation genetics has been slowed due, in large part, to concerns over how to apply methods developed for population-based sampling to individual-based sampling schemes. Here, we discuss the benefits of individual-based sampling for conservation and describe how landscape genomic methods, paired with individual-based sampling, can answer fundamental conservation questions. We have curated key landscape genomic methods into a user-friendly, open-source workflow, which we provide as a new R package, A Landscape Genomics Analysis Toolkit in R (algatr). The algatr package includes novel added functionality for all of the included methods and extensive vignettes designed with the primary goal of making landscape genomic approaches more accessible and explicitly applicable to conservation biology.

Keywords: conservation biology; conservation genetics; genetic diversity; landscape genomics; population structure; spatial analysis.

FIGURE 1

Summary of outputs produced by the algatr pipeline with suggested methods to answer conservation questions. Methods included in algatr's workflow are TESS (Caye et al., 2016), wingen (Bishop et al., 2023), multiple matrix regression with randomization (MMRR; Wang, 2013), generalized dissimilarity modelling (GDM; Ferrier et al., ; Fitzpatrick & Keller, ; Freedman et al., 2010), redundancy analysis (RDA; Capblancq & Forester, 2021) and latent factor mixed modelling (LFMM; Caye et al., 2019).