I have included the rest of the paper that I posted the abstract of yesterday. That includes all of the tables and statistical analysis: none of which I claim to understand but, as they are tried and tested models, and Dr Jones has had multiple papers published in which he has used those analytical tools, I expect the results to be accurate. I have also included all of the acknowledgements / references at the end. The references seem to go on forever and even include my least favourite paper by Shutt et al, for reasons that I have expounded at length elsewhere, particularly my piece “Feeding Blue Tits in your Garden, a Good or a Bad Thing?”
However, as Will referenced it in the text, it would have been dishonest to excise it.
Introduction
Adult survival is a key metric contributing to demographic trends and population viability (Gaillard et al., 1998; Siriwardena et al., 1998). Increases in adult mortality reduce the number of breeding-aged individuals in a population which can have a major knock-on effect on recruitment and subsequent population trends (Buehler et al., 2008; Weiser et al., 2020). Adult survival can differ between species, due to a variety reasons, such as body size (Sæther, 1989), differential predation rates (Sundell et al., 2003), disease risk and susceptibility (Kulma et al., 2013) or reproductive investment (Ghalambor & Martin, 2000; Karlsson et al., 1990). This can be the case for even closely related species occupying similar niches and living in sympatry (Camphuysen & Gronert, 2012; Eggers & Low, 2014; Imlay et al., 2022; Jones et al., 2022).
Woodland bird species in the United Kingdom have experienced mixed fortunes. Many species, particularly long-distance migrants have undergone massive population declines in recent years (Fuller et al., 2005; Gregory et al., 2007). This has been variously linked to insect declines and habitat degradation in both Europe and in their non-breeding ranges (Bowler et al., 2019; Fuller et al., 2005). While some species have declined, other species have increased, particularly resident and generalist species, which exhibit greater niche plasticity (Gregory et al., 2007). However, not all species follow these general patterns, and the tit family (Paridae) exemplifies this particularly well. Of the six species that breed in the United Kingdom, four species (Coal Tit, Periparus ater,Crested Tit Lophophanes cristatus, Blue Tit Cyanistes caeruleus and Great Tit Parus major) have stable or increasing population trends, while two species (Marsh Tit Poecile palustris and Willow Tit Po. montanus) are declining (Hewson et al., 2007; Siriwardena et al., 1998; Stanbury et al., 2021).
In this study, we explored annual adult survival trends in four of the six British tit species, breeding in sympatry in a mosaic woodland habitat at Braydon Forest, Wiltshire, England. While all four species co-occur and will readily form mixed flocks in the non-breeding season (Farine et al., 2012), competition for nesting sites in the breeding season is intense, and the larger Great Tit and the more aggressive Blue Tit will generally outcompete the smaller Coal and Marsh Tits (Gamelon et al., 2019; Perrins, 1979). The four species also engage in different breeding strategies, with Marsh Tits generally having single broods, whereas Blue, Great and Coal Tits will often have multiple broods in a season (Blondel, 1985; Harrap & Quinn, 1995; Nomi et al., 2017; Perrins, 1979). Species with higher annual fecundity are often thought to trade-off this resource expenditure with lower lifespans. For instance, previous studies have found generally low survival estimates and that expected lifespans for Blue Tits in Continental Europe do not exceed two years (Amininasab et al., 2017; Gyurácz et al., 2022; Podmokła et al., 2017). Few published studies have attempted to explore adult survival in Marsh Tits, although predicted lifespans in a short-term Swiss population were comparable to those found in Blue Tits (Schaub & Amann, 2001).
Marsh Tits declines have been pinned on a variety of causes, such as habitat fragmentation, competition for nest sites and predation (Broughton & Hinsley, 2015; Smith, 1993). Whether changes in adult survival are contributing to population trends in this species remain unknown. In many cases, adult survival rates do not directly impact population trends. For instance endangered Madagascar Plovers, Charadrius thoracicus have very high adult survival rates, despite ongoing population declines (Jones et al., 2022). However, this is not always the case, with high female mortality in Swift Parrots Lathamus discolor contributing towards population declines in that species (Heinsohn et al., 2015). Therefore, an accurate assessment of adult survival is crucial to understanding population trends and viability.
In this study, we aimed to see whether adult survival rates are different between Blue, Coal, Great and Marsh Tits in the Braydon Forest, and in particular, whether changes in their mortality trajectories on both the cross-sectional and longitudinal scales could be found. We hypothesised that the four species would have differing survival rates and mortality trajectories with high survival rates for Great (due to their size) and Marsh Tits (due to their lower clutch sizes) and lowest survival rates for Blue and Coal Tits, due to their small sizes and large clutch sizes. We also predicted that we would find annual apparent adult survival to increase over time, with milder winters allowing for higher survival in the non-breeding season.
Methods
The four tit species were monitored regularly between 2009-2019 at multiple sites across the Braydon Forest area in northern Wiltshire, England (51°34′ N 1°58′ W). Birds were caught and ringed during regular, standardised netting surveys across this period. Between 2009 and late 2012, most of the focus was on the site Ravensroost Woods. After that, with permission from Forestry Commission England in England and the Wiltshire Wildlife Trust, the focus was expanded to cover the other woodlands, marked on the map in Figure S1. Each site was visited multiple times each year across all seasons. Supplementary feeding, comprising peanuts and mixed seeds, was provided between November and March in every year at each site. Bird age was assessed using standardised aging criteria (Svensson, 1992). In total 5076 individuals, amounting to 2893 Blue Tits, 509 Coal Tits, 1569 Great Tits and 105 Marsh Tits were ringed and released with subsequent recaptures noted to assess survival.
Survival analyses were performed in the program MARK (White & Burnham, 1999). We used a time-specific Cormack-Jolly-Seber (CJS) modelling framework to estimate constant and annual probabilities of apparent survival (φ) and encounter probability (p). Apparent survival was defined as the probability of a marked individual surviving and returning to the study site between consecutive years. Yearly encounter histories for all individuals were constructed, where 0 = undetected and 1 = recaptured or resighted at the study site during a 12-month period. For each species ran 4 models: a model with constants for survival and encounter rate: φ(.) p(.); a model with time dependent survival and a constant encounter rate: φ(t) p(.); a model with constant survival and time dependent encounter rate: φ(.) p(t); and finally, time dependence in both survival and encounter rate φ(t) p(t). This final model served as our global model. Model fit for the global model was assessed using the parametric bootstrapping and median-ĉ procedures in MARK. To rank the candidate models, we used the quasi‐Akaike information criterion corrected for small sample sizes (QAICc). Models with the strongest support were identified as those with normalised Akaike weights (wi) ≥ 0.15 or ΔQAICc values ≤ 2 differences between QAICc of the model with lowest QAICc and the model under consideration (Burnham et al., 2011). All φ and p statistics are presented with ± standard error to 3 decimal places, unless otherwise stated. Estimates of the variance inflation factor (ĉ) for three species did not suggest the presence of over dispersion (Blue Tit = 0.99; Coal Tit = 1.02; Great Tit = 1.06), while estimates for Marsh Tit showed slight evidence for over dispersion (4.53). Species-specific estimates of apparent lifespan ( ) were calculated using the formula where = the estimate of apparent survival estimate from the model φ(.) p(t) (the highest ranked model for all species bar Coal Tit) respectively (Brownie et al., 1985). Standard errors were calculated using the delta method, which approximates sampling variance when the desired demographic parameter is a function of at least one other demographic parameter (Powell, 2007).
To better understand mortality trajectories, we used a Bayesian Survival Trajectory Analysis (BaSTA) (Colchero et al., 2012). BaSTA uses Markov-Chain Monte Carlo (MCMC) procedures to optimise mortality distributions, estimating the slope with which mortality increases with age. BaSTA estimates the recapture rate and uses that to correct the survival estimates extracted from the survival models. The survival analyses assume no dispersal since all four tit species have high site fidelity and low dispersal propensity, contrary to many natural populations (Dingemanse et al., 2003; Harvey et al., 1979; Nilsson, 1989). In addition, while year of death is unknown for all individuals in this study, birth year is known for 50.8% of individuals, therefore we can be confident that the mortality distribution parameters are reliable (Colchero & Clark, 2012; Spagopoulou et al., 2020).
We explored mortality trajectories in the tit community using Weibull, Gompertz and logistic models with either a simple shape or the more complex Makeham constant or bathtub shape and an exponential model with a simple shape. For more information, see Colchero, Jones, and Rebke (Colchero et al., 2012). We performed four parallel simulations that ran for 500,000 iterations with a burn-in of 10,000 and sampling every 500th chain. The ten models were then ranked according to their deviance information criteria (DIC) scores (Spiegelhalter et al., 2002).
Model comparisons indicated that a logistic model (Pletcher, 1999; Vaupel et al., 1979) with a bathtub shape (Siler, 1979) had the strongest support (Table S1). The bathtub shape adds a declining Gompertz function and constant to the basic logistic survival function (Siler, 1979). A logistic bathtub model provides two alpha parameters (a0 and a1) which describe the exponential decline that can be observed soon after marking. Three beta parameters are also produced (b0, b1, b2) which describe different parts of the logistic increase in mortality rates with age, with b0 describing and age independent, baseline mortality; b1 describing the initial exponential increase in mortality with age and b2 describing the degree of deceleration in mortality with age and the level of the asymptote (Colchero & Clark, 2012; Spagopoulou et al., 2020).
To assess differences in mortality trajectories between the four species, we used Kullback-Leibler divergence calibration (KLDC) (Burnham & Anderson, 2001; Kullback & Leibler, 1951). Values close to 0.5 suggest minimal differences between distributions, whereas values closer to 1 suggest major differences. In line with other studies, we considered a KLDC value > 0.800 to indicate a substantial difference between any two posterior distributions that are being compared (Hooper et al., 2017; Hudson et al., 2019; McDonald et al., 2014).
Results
We found minor differences in apparent adult survival in all four species, with Marsh Tits having significantly higher apparent survival rates than either Blue or Great Tits. There was no significant difference in survival between Blue Tits and Great Tits or Coal Tits and the other three species (Figure 1; Table 2). We found non-significant declines in annual survival over the period of the study for Coal and Marsh Tits and non-significant increases in annual survival for Blue and Great Tits. Life expectancies calculated from these apparent survival estimates ranged from 1.50 (± 0.83) years for Marsh Tits, 1.32 (± 0.41) years for Coal Tits, 1.07 (± 0.20) years for Blue Tits and 1.00 (± 0.23) years for Great Tits.
We found clear differences in mortality trajectories between all four species (Figure 2; Tables 2 & S2). Marsh Tits showed lowest baseline mortality (b0), followed by Coal Tits and Great Tits, with Blue Tits having the highest. However, this pattern was completely reversed for initial mortality trajectories (b1), with Marsh Tits having the highest early mortality trajectory and Blue Tits the shallowest. The deceleration in mortality (b2) was also substantially different between the four species and followed the same patterns as b1, however the differences were smaller than the other beta parameters.
Discussion
We discovered notable differences in survival rate and life expectancy in all four species (Figure 1). Interestingly, Marsh Tits had the highest survival rate and Great and Blue Tits the lowest. Our results suggest that Marsh Tits have a life expectancy of 1.5 years post fledging, while Great Tits have a life expectancy of just 1 year. This was surprising as Great Tits are the largest-bodied of the four tit species and therefore we would have expected to see higher survival rates in this species (Sæther, 1989). One driver of mortality in Great Tits could be the increase in avian pox infections documented in this species in recent decades (Lachish et al., 2012). While this virus is able to infect all four species, Great Tits appear to be particularly susceptible to the disease (Lawson et al., 2012). Coupled with this, Great Tits and Blue Tits are two of the most successful species in woodland bird guilds, with increasing population trends in the United Kingdom due to their abilities to exploit novel resources, such as nest boxes and garden feeders (Chamberlain et al., 2007; Francis et al., 2018; Plummer et al., 2019; Shutt et al., 2018). While Coal Tits, and to a lesser extent, Marsh Tits have managed to exploit garden feeders, both species are likely dominated by the more aggressive Blue and Great Tits in interspecific interactions (Francis et al., 2018; Shutt & Lees, 2021). This can then have a knock-on effect on survival rates, particularly overwinter survival (Broggi et al., 2022; Orell, 1989). Although supplemental food was not provided year-round, it was provided during the non-breeding season and therefore it may have artificially increased winter survival in the four species. Yet, while many of the ringing locations are located far from the nearest known garden feeders, tit species have been found to travel relatively long distances to supplement their diet (Shutt et al., 2021) and therefore some individuals in the Braydon Forest could be benefiting from additional supplementary feeding.
We found no strong evidence of temporal trends in survival for any of the four species, although there were marginal, non-significant increases in survival for Blue and Great Tits and non-significant declines in survival of Coal and Marsh Tits over time (Figure 1). Furthermore, survival models with explicit time components only had a top QAICc ranking for Coal Tits (Table 1). The difference in model rankings for Coal Tits compared to the other three species may be an artifact of their short overall lifespans, coupled with their slightly different habitat requirements. Coal Tits generally prefer mixed, or coniferous dominated forests as breeding locations, which are more patchily distributed in the Braydon Forest. Given that the majority of captures for this species occurred during the winter, where birds disperse from their core breeding areas, the temporal signature in survival could be linked to environmental factors driving dispersal. While Coal Tits in deciduous forests in the United Kingdom have been shown to be rather sedentary (Broughton et al., 2019), coniferous populations tend to be more dispersive (Ekman, 1989; Mckenzie et al., 2007). As their expected lifespans do not exceed two years, then between-year fluctuations in environmental conditions, and by extension dispersal, may explain this apparent temporal signature in Coal Tits.
We found substantial differences in survival and mortality trajectories for the four species (Figure 2). While baseline mortality was substantially lower in Marsh Tits, with the species was also slowest in reaching the mortality asymptote, initial mortality was higher. This suggests that Marsh Tits pay a substantially higher survival cost as new adults, but that the individuals that survive beyond those initial months have lower mortality probabilities as older adults. Blue Tits and Great Tits showed the opposite pattern, by having particularly high increases in mortality after the initial phase, and a high baseline mortality rate. Meanwhile, Coal Tits has similar initial mortality rates to Blue and Great Tits but substantially slower mortality subsequent mortality trajectories- although this trajectory was still substantially higher than Marsh Tits. This suggests that there are different demographic selection pressures on each of the four species. There is potentially higher selection for large clutch sizes and higher interspecific aggression at nesting sites for Blue and Great Tits, as their chances of surviving beyond a single breeding season are lower than Coal or Marsh Tits. Indeed, both Blue and Great Tits have been shown to be significantly more aggressive to nest competitors during the breeding season (Samplonius, 2019; Slagsvold & Wiebe, 2021; Velasco et al., 2021). Future studies should explore how inter- and intraspecific aggression is correlated with reproductive effort, survival, and longevity in the four species.
In conclusion, we find significant differences in survival and mortality for the four tit species breeding in the Braydon Forest. We suggest that these survival differences have arisen due to different life-history strategies, with Marsh Tits likely selected to have longer lifespans and lower annual fecundity, while Blue and Great Tits opt for higher fecundity and lower annual survival. The surprisingly low survival rates and high mortality trajectories experienced by Great Tits may be a sign of increases in pathogens in this species in recent decades. Our study further highlights how adult survival is a poor predictor of population trends and that future studies and conservation measures should focus on other life stages to mitigate against declines in Marsh Tit numbers.
Charts & Tables
Figure 1. Annual survival rates for each of the four tit species breeding in Braydon Forest. We find significantly higher apparent annual survival for Marsh Tits than either Blue or Great Tits. Coal Tits had intermediate apparent survival rates. Furthermore, while we find decreases in annual survival for Coal and Marsh Tits and increases in annual survival for Blue and Great Tits, these trends are not significant.
Figure 2. Mortality trajectories fitted to a logistic model with a bathtub shape for the four tit species at Braydon Forest. The six plots on the left denote the posterior distributions of the 6 model parameters (see Methods for details).
Table 1. Model rankings for the four tit species. “φ” denotes apparent survival, “p” denotes recapture probability, “.” is constant and “t” is time dependent (year). Model fit is described by the number of parameters (K), the devience (QDeviance) and the difference in quasi-Akaike’s information criterion from the best-fit model (ΔQAICc). QAICc values were calculated using a median-ĉ of 0.99 for Blue Tit, 1.02 for Coal Tit, 1.06 for Great Tit and 4.53 for Marsh Tit. Note that as the asymptotic value of ĉ = 1, the estimate for Blue Tit was set to 1, and therefore, AICc was used instead of QAICc
| Species | Model | K | QDeviance | QAICc | ΔQAICc |
| Blue Tit | φ(.) p(t) | 11 | 94.84 | 3547.22 | 0.00 |
| Blue Tit | φ(t) p(t) | 19 | 87.99 | 3556.51 | 9.30 |
| Blue Tit | φ(t) p(.) | 11 | 123.75 | 3576.12 | 28.90 |
| Blue Tit | φ(.) p(.) | 2 | 172.43 | 3606.73 | 59.51 |
| Coal Tit | φ(t) p(t) | 16 | 74.79 | 867.26 | 0.00 |
| Coal Tit | φ(.) p(t) | 9 | 95.15 | 873.05 | 5.80 |
| Coal Tit | φ(t) p(.) | 11 | 117.76 | 899.79 | 32.54 |
| Coal Tit | φ(.) p(.) | 2 | 167.31 | 930.96 | 63.71 |
| Great Tit | φ(.) p(t) | 11 | 118.05 | 1864.36 | 0.00 |
| Great Tit | φ(t) p(t) | 18 | 110.54 | 1871.09 | 6.72 |
| Great Tit | φ(t) p(.) | 11 | 131.58 | 1877.90 | 13.53 |
| Great Tit | φ(.) p(.) | 2 | 164.97 | 1893.14 | 28.78 |
| Marsh Tit | φ(.) p(t) | 8 | 72.56 | 341.13 | 0.00 |
| Marsh Tit | φ(t) p(t) | 15 | 62.24 | 347.16 | 6.03 |
| Marsh Tit | φ(.) p(.) | 2 | 91.85 | 347.56 | 6.43 |
| Marsh Tit | φ(t) p(.) | 11 | 81.62 | 357.01 | 15.88 |
Table 2. Survival (φ) and recapture (p) estimates with standard error (SE) and 95% confidence intervals (CI) for each species for the φ(.) p(t) model and φ(t) p(t) for Coal Tit.
| Species | Parameter | Estimate | SE | 95% CI |
| Blue Tit | φ | 0.393 | 0.017 | 0.067 |
| p1 | 0.320 | 0.081 | 0.310 | |
| p2 | 0.541 | 0.108 | 0.400 | |
| p3 | 0.760 | 0.088 | 0.340 | |
| p4 | 0.287 | 0.040 | 0.157 | |
| p5 | 0.269 | 0.031 | 0.121 | |
| p6 | 0.140 | 0.024 | 0.094 | |
| p7 | 0.273 | 0.036 | 0.140 | |
| p8 | 0.327 | 0.046 | 0.179 | |
| p9 | 0.398 | 0.045 | 0.174 | |
| p10 | 0.321 | 0.038 | 0.149 | |
| Coal Tit | φ1 | 0.412 | 0.121 | 0.442 |
| φ2 | 0.547 | 0.163 | 0.565 | |
| φ3 | 0.435 | 0.105 | 0.389 | |
| φ4 | 0.484 | 0.114 | 0.418 | |
| φ5 | 1.000 | 0.001 | 0.999 | |
| φ6 | 0.426 | 0.130 | 0.471 | |
| φ7 | 0.233 | 0.060 | 0.236 | |
| φ8 | 0.598 | 0.112 | 0.413 | |
| φ9 | 0.438 | 0.217 | 0.693 | |
| φ10 | 0.088 | 2.431 | 1.000 | |
| p1 | 1.000 | 0.000 | 0.000 | |
| p2 | 0.753 | 0.206 | 0.706 | |
| p3 | 1.000 | 0.000 | 0.000 | |
| p4 | 0.561 | 0.143 | 0.508 | |
| p5 | 0.196 | 0.038 | 0.150 | |
| p6 | 0.194 | 0.074 | 0.289 | |
| p7 | 0.778 | 0.109 | 0.420 | |
| p8 | 0.764 | 0.138 | 0.515 | |
| p9 | 0.286 | 0.150 | 0.541 | |
| p10 | 0.805 | 22.271 | 1.000 | |
| Great Tit | φ | 0.369 | 0.022 | 0.085 |
| p1 | 0.235 | 0.077 | 0.299 | |
| p2 | 0.493 | 0.155 | 0.543 | |
| p3 | 0.840 | 0.197 | 0.762 | |
| p4 | 0.411 | 0.072 | 0.276 | |
| p5 | 0.201 | 0.039 | 0.154 | |
| p6 | 0.176 | 0.040 | 0.159 | |
| p7 | 0.407 | 0.065 | 0.248 | |
| p8 | 0.462 | 0.067 | 0.257 | |
| p9 | 0.457 | 0.066 | 0.252 | |
| p10 | 0.392 | 0.064 | 0.245 | |
| Marsh Tit | φ | 0.512 | 0.048 | 0.222 |
| p1 | 1.000 | 0.000 | 1.000 | |
| p2 | 0.262 | 0.231 | 0.755 | |
| p3 | 1.000 | 0.000 | 1.000 | |
| p4 | 0.637 | 0.187 | 0.631 | |
| p5 | 0.342 | 0.173 | 0.598 | |
| p6 | 1.000 | 0.000 | 0.000 | |
| p7 | 0.678 | 0.141 | 0.510 | |
| p8 | 0.799 | 0.150 | 0.573 | |
| p9 | 0.475 | 0.171 | 0.586 | |
| p10 | 0.631 | 0.224 | 0.713 |
Table 3. Kullback-Leibler divergence calibration (KLDC) scores comparing parameter posterior distributions between each tit species. Substantial species-pair differences (KLDC > 0.800) are highlighted in bold.
| Species comparison | ɑ0 | ɑ1 | c | b0 | b1 | b2 |
| Coal – Blue | 0.946 | 0.536 | 0.955 | 1.000 | 1.000 | 0.989 |
| Great – Blue | 0.667 | 0.506 | 0.786 | 1.000 | 0.999 | 0.949 |
| Great – Coal | 0.753 | 0.513 | 0.891 | 1.000 | 1.000 | 0.722 |
| Marsh – Blue | 0.999 | 0.629 | NA | 1.000 | 1.000 | 1.000 |
| Marsh – Coal | 0.914 | 0.535 | 0.967 | 1.000 | 1.000 | 0.969 |
| Marsh – Great | 0.994 | 0.586 | NA | 1.000 | 1.000 | 0.994 |
Supplementary information
Figure S1. Distribution of the Braydon Forest wood plots in north Wiltshire, UK. (1. Ravensroost Wood; 2. Somerford Common; 3. Webb’s Wood; 4. The Firs; 5. Red Lodge.
Table S1. Model selection and comparison using the Deviance Information Criterion (DIC) between the 9 models tested with the BaSTA analysis. K indicates the number of parameters for each model. Note that a 10th model (Weibull with a simple shape) did not converge and is therefore excluded from the model output.
| Model | Shape | K | DIC | ΔDIC |
| Logistic | Bathtub | 25 | 16171 | 0 |
| Logistic | Simple | 13 | 16505 | 334 |
| Logistic | Makeham | 17 | 16728 | 557 |
| Weibull | Bathtub | 21 | 24853 | 8682 |
| Weibull | Makeham | 13 | 25711 | 9540 |
| Gompertz | Bathtub | 21 | 31891 | 15720 |
| Gompertz | Simple | 9 | 32281 | 16110 |
| Gompertz | Makeham | 13 | 32748 | 16577 |
| Exponential | Simple | 5 | 40993 | 24822 |
Acknowledgements
Thanks to the Wiltshire Wildlife Trust and Forestry England for providing ongoing and regular access to their sites, and to Forestry England for their regular financial support for the project. Further thanks are due to the ringing team members who have helped with the work over the period of the study.
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Ringing In The Braydon Forest & Beyond 