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Identifying the genetic architecture of complex phenotypes is a central goal of modern biology, particularly for disease-related traits. Genome-wide association methods are a classical approach for identifying the genomic basis of variation in disease phenotypes, but such analyses are particularly challenging in natural populations due to sample size difficulties. Extensive mark-recapture data, strong linkage disequilibrium and a lethal transmissible cancer make the Tasmanian devil (Sarcophilus harrisii) an ideal model for such an association study. We used a RAD-capture approach to genotype 624 devils at ~16,000 loci and then used association analyses to assess the heritability of three cancer-related phenotypes: infection case-control (where cases were infected devils and controls were devils that were never infected), age of first infection and survival following infection. The SNP array explained much of the phenotypic variance for female survival (>80%) and female case-control (>61%). We found that a few large-effect SNPs explained much of the variance for female survival (~5 SNPs explained >61% of the total variance), whereas more SNPs (~56) of smaller effect explained less of the variance for female case-control (~23% of the total variance). By contrast, these same SNPs did not account for a significant proportion of phenotypic variance in males, suggesting that the genetic bases of these traits and/or selection differ across sexes. Loci involved with cell adhesion and cell-cycle regulation underlay trait variation, suggesting that the devil immune system is rapidly evolving to recognize and potentially suppress cancer growth through these pathways. Overall, our study provided necessary data for genomics-based conservation and management in Tasmanian devils.

A central debate in ecology has been the long‐running discussion on the role of apex predators in affecting the abundance and dynamics of their prey. In terrestrial systems, research has primarily relied on correlational approaches, due to the challenge of implementing robust experiments with replication and appropriate controls. A consequence of this is that we largely suffer from a lack of mechanistic understanding of the population dynamics of interacting species, which can be surprisingly complex. Mechanistic models offer an opportunity to examine the causes and consequences of some of this complexity. We present a bioenergetic mechanistic model of a tritrophic system where the primary vegetation resource follows a seasonal growth function, and the herbivore and carnivore species are modeled using two integral projection models (IPMs) with body mass as the phenotypic trait. Within each IPM, the demographic functions are structured according to bioenergetic principles, describing how animals acquire and transform resources into body mass, energy reserves, and breeding potential. We parameterize this model to reproduce the population dynamics of grass, elk, and wolves in northern Yellowstone National Park (USA) and investigate the impact of wolf reintroduction on the system. Our model generated predictions that closely matched the observed population sizes of elk and wolf in Yellowstone prior to and following wolf reintroduction. The introduction of wolves into our basal grass–elk bioenergetic model resulted in a population of 99 wolves and a reduction in elk numbers by 61% (from 14,948 to 5823) at equilibrium. In turn, vegetation biomass increased by approximately 25% in the growing season and more than threefold in the nongrowing season. The addition of wolves to the model caused the elk population to switch from being food‐limited to being predator‐limited and had a stabilizing effect on elk numbers across different years. Wolf predation also led to a shift in the phenotypic composition of the elk population via a small increase in elk average body mass. Our model represents a novel approach to the study of predator–prey interactions, and demonstrates that explicitly considering and linking bioenergetics, population demography and body mass phenotypes can provide novel insights into the mechanisms behind complex ecosystem processes. [ABSTRACT FROM AUTHOR]

Major histocompatibility complex ( Mhc) genes are frequently used as a model for adaptive genetic diversity. Although associations between Mhc and disease resistance are frequently documented, little is known about the fitness consequences of Mhc variation in wild populations. Further, most work to date has involved testing associations between Mhc genotypes and fitness components. However, the functional diversity of the Mhc, and hence the mechanism by which selection on Mhc acts, depends on how genotypes map to the functional properties of Mhc molecules. Here, we test three hypotheses that relate Mhc diversity to fitness: (i) the maximal diversity hypothesis, (ii) the optimal diversity hypothesis and (iii) effect of specific Mhc types. We combine mark-recapture methods with analysis of long-term breeding data to investigate the effects of Mhc class I functional diversity ( Mhc supertypes) on individual fitness in a wild great tit ( Parus major) population. We found that the presence of three different Mhc supertypes was associated with three different components of individual fitness: survival, annual recruitment and lifetime reproductive success ( LRS). Great tits possessing Mhc supertype 3 experienced higher survival rates than those that did not, whereas individuals with Mhc supertype 6 experienced higher LRS and were more likely to recruit offspring each year. Conversely, great tits that possessed Mhc supertype 5 had reduced LRS. We found no evidence for a selective advantage of Mhc diversity, in terms of either maximal or optimal supertype diversity. Our results support the suggestion that specific Mhc types are an important determinant of individual fitness. [ABSTRACT FROM AUTHOR]

Summary 1. Reliable assessments of infection status and population prevalence are critical for epidemiological modelling and disease management, but can be greatly biased when disease state is determined from imperfect diagnostic tests. Available statistical methods to adjust test-based prevalence estimates by correcting for test accuracy demand that many stringent requirements and assumptions be met (knowledge about underlying population prevalence or multiple diagnostic methods), limiting their utility for wildlife disease surveys. 2. In this paper, we present site-occupancy modelling as a flexible approach to derive estimates of population prevalence and test sensitivity under imperfect pathogen detection without a need for restrictive requirements or assumptions. We extend the utility of the standard site-occupancy framework for pathogen detection data by novel application of abundance-induced heterogeneity (AIH) models (Royle & Nichols 2003) that allow test sensitivity to vary with host pathogen load or infection intensity. 3. We demonstrate the utility of this approach for wildlife disease studies by applying site-occupancy models to a data set consisting of replicate quantitative (q)PCR diagnoses of malaria parasites ( Plasmodium spp.) in blood samples from wild blue tits ( Cyanistes caeruleus). 4. Model selection revealed that Plasmodium detection rates by qPCR were strongly dependent on host parasite load. Estimates of parasite detection rates revealed the qPCR assay to be highly sensitive, with accordingly, a very low probability of false negative diagnosis for the majority of infected hosts in our population and little bias in naive estimates of population prevalence, although this will be a system-specific result. 5. Our results demonstrate the utility of a site-occupancy approach for deriving estimates of population prevalence under imperfect pathogen detection and reveal that accounting for host variation in pathogen load allows a more accurate assessment of diagnostic test sensitivity. 6. By identifying factors that influence pathogen detection rates, and revealing optimal protocols for obtaining unbiased prevalence estimates, while minimising the probability of false negative diagnoses, we also show that this approach can enhance both diagnostic accuracy and cost-efficiency in wildlife disease surveys. [ABSTRACT FROM AUTHOR]