Temporal patterns of TLR3 variation on Cousin
In total, 1,190 birds hatched on Cousin from four cohorts 1992–94, 1997–99, 2005–10, and 2016–18, were sequenced at the TLR3 SNP. The earliest and latest of the sampled cohorts were used to assess temporal changes. In addition, the years 1997–99 and 2005–10 were selected; (i) to avoid hatch years in which translocations happened (2004, 2011), as the subsequent reduction in population density may have a positive effect on juvenile (<1 year) survival in that year (Brouwer et al., 2006), and, (ii) to focus on individuals with the most complete MHC and life-history data. Temporal allelic variation was analysed using a linear model (LM) and significance was assessed using the F-statistic. Frequency of TLR3 c in the sampled adult or juvenile population was the response variable, while year was the fixed factor.
Contemporary selection on TLR3 variation on Cousin
Survival : A mixed‐effects Cox proportional hazards model in the package coxme 2.2-14 (Therneau, 2019), was used to determine whetherTLR3 genotypes differed in survival. Model diagnostics using Schoenfeld’s residuals confirmed that proportional hazards assumptions were met (Grambsch & Therneau, 1994). Age at death was standardised to bi-annual levels corresponding to the major and minor seasons. Fieldwork was not conducted for four minor breeding seasons (2000–2002, 2006), so accurate bi-annual survival estimates could not be calculated for 77 individuals. Instead, the minimum date of death was assigned (i.e., the last season an individual was observed). Excluding these individuals did not qualitatively alter the results, so they were retained in the model. Birds first caught as an adult (>1 year, n = 21) were excluded to prevent any survivorship bias from including individuals that have already survived the first year of life, and because Seychelles warblers cannot be reliably aged past one year of age (Wright, 2014). Individuals that were translocated to other islands (n = 39), and those still alive after the major 2018 breeding season (n = 42) were right-censored. Previous work has found that in low-quality seasons maternal heterozygosity affected offspring survival (Brouwer, Komdeur, & Richardson, 2007), and MHC diversity positively affected survival in juveniles, while individuals with the MHC class I allele (Ase-ua4 ) have a greater life expectancy (Brouwer et al., 2010). TLR3 genotype (TLR3 AA/TLR3 AC/TLR3 CC), MHC diversity (2–8 different alleles), presence of the Ase-ua4allele (Yes/No), individual heterozygosity (H s), maternal heterozygosity (Maternal H s), sex (Male/Female) and season in which born (Minor/Major) were included as fixed factors in the model, with hatch year included as a random factor. Individuals hatched on Cousin between 1997–99 or 2005–2010, for which these data were available, were included (n = 517). Cox proportional hazards models in the package survival 2.44-1.1 (Therneau & Lumley, 2015), without the random effects, were used to plot Kaplan–Meier survival curves.
Reproductive success: A zero-inflated generalised linear mixed model (GLMM) with a Poisson error structure was run using the package glmmTMB 0.2.3 (Brooks et al., 2017) to test whether lifetime reproductive success (LRS) was associated with TLR3 variation. LRS was measured as the number of offspring that survived to independence (3 months) throughout an individual’s lifespan. Both social and extra-pair offspring were included. Individuals that were translocated, or still alive after the minor 2018 season, were excluded due to incomplete data. Individuals first caught over one year of age, for which we did not have accurate age and longevity data, were also excluded. All other birds hatched on Cousin between 1997–99 and 2005–2010 were included (n = 487). TLR3 genotype, MHC diversity, presence of the Ase-ua4 allele, and individualH s were fixed factors in the model, with year of hatch as a random factor to control for cohort effects. The sexes were modelled separately as it is likely that different factors and constraints act upon males and females.
As LRS is strongly correlated with longevity (GLMM,P <0.001, Table 2), and survival was strongly correlated with TLR3 genotype (COXME, P = 0.026, Fig 2, Table 1), we tested if lifetime reproductive rate (defined as reproduction controlling for longevity) was associated with TLR3 genotype. The model and dataset used was the same as used for LRS, except for two key differences: (i) Individuals which died before reaching adulthood (i.e. 1 year of age) were excluded from this analysis (resulting in n = 323), (ii) Age at death (i.e. longevity and longevity2) were included as fixed factors. The inclusion of longevity, and the exclusion of non-adult individuals, allows reproductive success to be isolated from survival; thus gaining a measure of the rate of reproduction during the individual’s adult life.
For both LRS and rate of reproduction models all continuous factors were standardised (scaled and centred) using the package arm 1.10-1 (Gelman, Su, Masanao, Zheng, & Dorie, 2018). Collinearity between fixed effects was tested using variance inflation factors. We used the package DHARMA 0.2.4 (Hartig, 2017) to confirm that there was no over or under dispersion, residual spatial or temporal autocorrelation in the GLMM models. We used model averaging using the dredge function in the MUMIn package 1.43.6 (Barton & Barton, 2019) to select plausible models. All models within 7 AICc of the top model were included in the averaged model, to get the final conditional model.
Selection coefficient: Mean values of LRS were calculated for each genotype from the raw data, relative fitness per TLR3genotype was calculated by dividing the mean for all three genotypes by the mean from the genotype with the greatest fitness. The dataset used was the same as that used for LRS – except that mean LRS was measured as the total number of offspring produced by an individual that survived to recruitment (>1 year) as this is a more accurate measure of genotype contribution to the next generation..
Hardy-Weinberg Equilibrium in young birds on Cousin: Deviation from Hardy-Weinberg Equilibrium (HWE) was tested using exact tests (Guo & Thompson, 1992) based on allelic frequencies in Genepop 4.2 (Rousset, 2008). P values were estimated with Markov chain algorithms (1,000 dememorisations, 100 batches, 1,000 iterations), andF IS values are presented using Robertson and Hill estimates (Robertson & Hill, 1984). First, all birds from Cousin first caught before 3 months of age (before independence) were tested (n = 591). Second, to determine if early-life mortality changed HWE proportions, this test was repeated including only individuals that survived until adulthood (n = 361). To determine if any deviation from HWE was caused by a temporal Wahlund-like effect (as in Pusack, Christie, Johnson, Stallings, & Hixon, 2014) we also re-ran the analysis separately for each hatch year.