Landscape relatedness: detecting contemporary fine-scale spatial structure in wild populations

Abstract

Context Methods for detecting contemporary, finescale population genetic structure in continuous populations are scarce. Yet such methods are vital for ecological and conservation studies, particularly under a changing landscape.Objectives Here we present a novel, spatially explicit method that we call landscape relatedness (LandRel). With this method, we aim to detect contemporary, fine-scale population structure that is sensitive to spatial and temporal changes in the landscape. Methods We interpolate spatially determined relatedness values based on SNP genotypes across the landscape. Interpolations are calculated using the Bayesian inference approach integrated nested Laplace approximation. We empirically tested this method on a continuous population of brown bears (Ursus arctos) spanning two counties in Sweden. 123Landscape Ecol (2017) 32:181-194 DOI 10.1007 Results Two areas were identified as differentiated from the remaining population. Further analysis suggests that inbreeding has occurred in at least one of these areas. Conclusions LandRel enabled us to identify previously unknown fine-scale structuring in the population. These results will help direct future research efforts, conservation action and aid in the management of the Scandinavian brown bear population. LandRel thus offers an approach for detecting subtle population structure with a focus on contemporary, fine-scale analysis of continuous populations.

Species Distribution Modeling: Comparison of Fixed and Mixed Effects Models Using INLA

“…These methods have now been applied in ecology [55,62,112,137,[155][156][157], climate research [158] and health sciences [159][160][161] and have very promising application in invasion and forest sciences. Moreover, the use of this approach in constructing species distribution maps might help the design of integrated management programs.…”

Genetic and genomic monitoring with minimally invasive sampling methods

“…In such cases, it is important to know where these boundaries lie in order to infer the underlying demographic processes structuring the population(s), and to assign individuals to those populations using the correct allele frequency data. Over recent years, numerous studies have successfully investigated genetic structure in wild populations using MIS (e.g., Norman et al., ; Russello, Waterhouse, Etter, & Johnson, ; Steyer et al., ). Different approaches have been developed to investigate the genetic structuring of a group or population, using either multivariate analysis (Jombart, Pontier, & Dufour, ) or Bayesian methods for optimizing population features such as Hardy–Weinberg equilibrium (Pritchard, Stephens, & Donnelly, ) and even allowing for the integration of environmental and spatial data for interpretation purposes (e.g., Caye, Deist, Martins, Michel, & Francois, ; Guillot, Mortier, & Estoup, ).…”

“…In such cases, it is important to know where these boundaries lie in order to infer the underlying demographic processes structuring the population(s), and to assign individuals to those populations using the correct allele frequency data. Over recent years, numerous studies have successfully investigated genetic structure in wild populations using MIS (e.g., Norman et al, 2017;Russello, Waterhouse, Etter, & Johnson, 2015;Steyer et al, 2016).…”

Genotyping‐by‐sequencing for estimating relatedness in nonmodel organisms: Avoiding the trap of precise bias

“…However, there has been remarkably little attention to using genotyping‐by‐sequencing for research that especially requires high resolution, such as questions that require identifying related individuals in the absence of a known pedigree. Ecologically relevant research that would benefit from identifying related individuals includes investigations of breeding behaviour, social structure, inbreeding and inbreeding depression, and population demographics and connectivity (Coleman & Jones, ; Iacchei et al., ; Kjeldsen et al., ; Möller, ; Norman et al., ; Palsbøll, Peery, & Bérubé, ; Peery et al., ; Ross, ). Related individuals can be identified by estimating relatedness or inferring kinship categories: relatedness is a continuous measure of the proportion of alleles in a dyad (i.e., in a pair of individuals, regardless of whether the pair was observed together) that are identical by descent (IDB) relative to a reference population; kinship categories are discrete categories of dyad relationship such as parent–offspring or full‐sibling.…”