Population structure and gene flow in a newly harvested gray wolf (Canis lupus) population

The effects of human-caused mortality on mammalian cooperative breeders: a synthesis

Human-caused mortality can be pervasive and even highly selective for individuals in groups of cooperative breeders. Many studies of cooperative breeders, however, do not address human-caused mortality. Similarly, studies focused on the effects of human-caused mortality on wildlife populations often do not consider the ecology of cooperative breeders. We searched the literature and identified 58 studies where human-caused mortality affected a group characteristic, vital rate, or population state of a cooperative breeder. Of studies reporting population growth or decline, 80% reported a link between human-caused mortality and population declines in cooperative breeders. Such studies often did not identify the mechanism behind population declines, but 28% identified concurrent declines in adult survival and another 21% reported concurrent declines in recruitment or reproduction. There was little overlap between the cooperative breeding and human-caused mortality literatures, limiting our ability to accrue knowledge. Future work would be beneficial if it (i) identified the vital rate(s) causing population declines, (ii) leveraged management actions such as lethal removal to ask questions about the ecology of group-living in cooperative breeders, and (iii) used insights from cooperative breeding theory to inform management actions and conservation of group-living species.

Demographic history shapes North American gray wolf genomic diversity and informs species' conservation

Effective population size estimates are critical information needed for evolutionary predictions and conservation decisions. This is particularly true for species with social factors that restrict access to breeding or experience repeated fluctuations in population size across generations. We investigated the genomic estimates of effective population size along with diversity, subdivision, and inbreeding from 162,109 minimally filtered and 81,595 statistically neutral and unlinked SNPs genotyped in 437 grey wolf samples from North America collected between 1986 and 2021. We found genetic structure across North America, represented by three distinct demographic histories of western, central, and eastern regions of the continent. Further, grey wolves in the northern Rocky Mountains have lower genomic diversity than wolves of the western Great Lakes and have declined over time. Effective population size estimates revealed the historical signatures of continental efforts of predator extermination, despite a quarter century of recovery efforts. We are the first to provide molecular estimates of effective population size across distinct grey wolf populations in North America, which ranged between Ne  ~ 275 and 3050 since early 1980s. We provide data that inform managers regarding the status and importance of effective population size estimates for grey wolf conservation, which are on average 5.2-9.3% of census estimates for this species. We show that while grey wolves fall above minimum effective population sizes needed to avoid extinction due to inbreeding depression in the short term, they are below sizes predicted to be necessary to avoid long-term risk of extinction.

Genetic Monitoring of Grey Wolves in Latvia Shows Adverse Reproductive and Social Consequences of Hunting

Nowadays, genetic research methods play an important role in animal population studies. Since 2009, genetic material from Latvian wolf specimens obtained through hunting has been systematically gathered. This study, spanning until 2021, scrutinizes the consequences of regulated wolf hunting on population genetic metrics, kinship dynamics, and social organization. We employed 16 autosomal microsatellites to investigate relationships between full siblings and parent-offspring pairs. Our analysis encompassed expected and observed heterozygosity, inbreeding coefficients, allelic diversity, genetic distance and differentiation, mean pairwise relatedness, and the number of migrants per generation. The Latvian wolf population demonstrated robust genetic diversity with minimal inbreeding, maintaining stable allelic diversity and high heterozygosity over time and it is not fragmented. Our findings reveal the persistence of conventional wolf pack structures and enduring kinship groups. However, the study also underscores the adverse effects of intensified hunting pressure, leading to breeder loss, pack disruption, territorial displacement, and the premature dispersal of juvenile wolves.

Patterns and determinants of dispersal in grey wolves (Canis lupus)

Dispersal is a key demographic process involving three stages: emigration, transience and settlement; each of which is influenced by individual, social and environmental determinants. An integrated understanding of species dispersal is essential for demographic modelling and conservation planning. Here, we review the dispersal patterns and determinants documented in the scientific literature for the grey wolf (Canis lupus) across its distribution range. We showed a surprisingly high variability within and among study areas on all dispersal parameters - dispersal rate, direction, distance, duration and success. We found that such large variability is due to multiple individual, social and environmental determinants, but also due to previously overlooked methodological research issues. We revealed a potential non-linear relationship between dispersal rate and population density, with dispersal rate higher at both ends of the gradient of population density. We found that human-caused mortality reduces distance, duration and success of dispersal events. Furthermore, dispersers avoid interaction with humans, and highly exposed areas like agricultural lands hamper population connectivity in many cases. We identified numerous methodological research problems that make it difficult to obtain robust estimates of dispersal parameters and robust inferences on dispersal patterns and their determinants. In particular, analyses where confounding factors were not accounted for led to substantial knowledge gaps on all aspects of dispersal in an otherwise much-studied species. Our understanding of wolf biology and management would significantly benefit if wolf dispersal studies reported the results and possible factors affecting wolf dispersal more transparently.

Closer to Carrying Capacity: Analysis of the Internal Demographic Structure Associated with the Management and Density Dependence of a Controlled Wolf Population in Latvia

Large carnivores are essential components of natural ecosystems. In populated areas, their conservation depends on preserving a favorable status in coexistence with humans, which may require the elimination of excess carnivores to minimize public concerns. As the Baltic region currently hosts a thriving wolf population, locally sustainable management of wolves is important for preserving biodiversity at a European scale. In this paper, we provide a dynamic assessment of the Latvian wolf subpopulation from 1998 until 2020. This study is based on age composition and fecundity data from teeth, uteri, and ovaria inspections obtained from samples of legally culled or accidentally killed individuals. The abundance estimates indicated population growth that exceeded the previously predicted carrying capacity. The proportion of juveniles among the culled individuals increased in recent years, but the mean age of culled adults exhibited a stable trend. In presumably nonselective hunting, the juveniles and individuals older than 3 years had greater culling mortality estimates in comparison with other age classes, and the culling rates for adult females of particular age classes were higher than for males of the same age. While creating significant hunting pressure, wolf management in Latvia may have contributed to the population growth by affecting its demographic processes.

Does harvest affect genetic diversity in grey wolves?

Harvest can affect vital rates such as reproduction and survival, but also genetic measures of individual and population health. Grey wolves (Canis lupus) live and breed in groups, and effective population size is a small fraction of total abundance. As a result, genetic diversity of wolves may be particularly sensitive to harvest. We evaluated how harvest affected genetic diversity and relatedness in wolves. We hypothesized that harvest would (a) reduce relatedness of individuals within groups in a subpopulation but increase relatedness of individuals between groups due to increased local immigration, (b) increase individual heterozygosity and average allelic richness across groups in subpopulations and (c) add new alleles to a subpopulation and decrease the number of private alleles in subpopulations due to an increase in breeding opportunities for unrelated individuals. We found harvest had no effect on observed heterozygosity of individuals or allelic richness at loci within subpopulations but was associated with a small, biologically insignificant effect on within-group relatedness values in grey wolves. Harvest was, however, positively associated with increased relatedness of individuals between groups and a net gain (+16) of alleles into groups in subpopulations monitored since harvest began, although the number of private alleles in subpopulations overall declined. Harvest likely created opportunities for wolves to immigrate into nearby groups and breed, thereby making groups in subpopulations more related over time. Harvest appears to affect genetic diversity in wolves at the group and population levels, but its effects are less apparent at the individual level given the population sizes we studied.

Detecting geospatial patterns of Plasmodium falciparum parasite migration in Cambodia using optimized estimated effective migration surfaces

BACKGROUND

Understanding the genetic structure of natural populations provides insight into the demographic and adaptive processes that have affected those populations. Such information, particularly when integrated with geospatial data, can have translational applications for a variety of fields, including public health. Estimated effective migration surfaces (EEMS) is an approach that allows visualization of the spatial patterns in genomic data to understand population structure and migration. In this study, we developed a workflow to optimize the resolution of spatial grids used to generate EEMS migration maps and applied this optimized workflow to estimate migration of Plasmodium falciparum in Cambodia and bordering regions of Thailand and Vietnam.

METHODS

The optimal density of EEMS grids was determined based on a new workflow created using density clustering to define genomic clusters and the spatial distance between genomic clusters. Topological skeletons were used to capture the spatial distribution for each genomic cluster and to determine the EEMS grid density; i.e., both genomic and spatial clustering were used to guide the optimization of EEMS grids. Model accuracy for migration estimates using the optimized workflow was tested and compared to grid resolutions selected without the optimized workflow. As a test case, the optimized workflow was applied to genomic data generated from P. falciparum sampled in Cambodia and bordering regions, and migration maps were compared to estimates of malaria endemicity, as well as geographic properties of the study area, as a means of validating observed migration patterns.

RESULTS

Optimized grids displayed both high model accuracy and reduced computing time compared to grid densities selected in an unguided manner. In addition, EEMS migration maps generated for P. falciparum using the optimized grid corresponded to estimates of malaria endemicity and geographic properties of the study region that might be expected to impact malaria parasite migration, supporting the validity of the observed migration patterns.

CONCLUSIONS

Optimized grids reduce spatial uncertainty in the EEMS contours that can result from user-defined parameters, such as the resolution of the spatial grid used in the model. This workflow will be useful to a broad range of EEMS users as it can be applied to analyses involving other organisms of interest and geographic areas.