# Multidimensional plasticity of phenology: assessing the effects of population density on plastic responses of breeding time to temperature

Vaishnavi Purushotham, Paul Cuchot, Celine Teplitsky, Erik Matthysen

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Vaishnavi Purushotham, Paul Cuchot, Celine Teplitsky, Erik Matthysen. Multidimensional plasticity of phenology: assessing the effects of population density on plastic responses of breeding time to temperature. Oikos, 2025, 2025 (6), pp.e10959. 10.1111/oik.10959. hal-05345181

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OIKOS

Research article

# Multidimensional plasticity of phenology: assessing the effects of population density on plastic responses of breeding time to temperature

Vaishnavi Purushotham<sup>1,2</sup>, Paul Cuchot<sup>1</sup>, Celine Teplitsky<sup>1</sup> and Erik Matthysen<sup>1,2</sup>

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**Correspondence:** Vaishnavi Purushotham (vp.cnrs@gmail.com)

Oikos

2025: e10959

doi: 10.1111/oik.10959

Subject Editor: Pedro Peres-Neto

Editor-in-Chief: Pedro Peres-Neto

Accepted 7 January 2025

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Phenotypic plasticity is the key adaptive mechanism behind annual adjustment of breeding time in response to temperature. In nature, organisms are not only subjected to variation in temperature but encounter multiple fluctuating environmental factors that affect phenotypic expression, including conspecific density, which affects individual performances through resource competition. We examined the interactive effects of temperature and conspecific density at two spatial scales (territory and patch level) on breeding time and success utilizing data obtained from long-term monitoring of a wild great tit *Parus major* population in a fragmented woodland. As expected, we detected earlier breeding in response to warmer spring temperatures. We report earlier laying at low territory level density (i.e. a larger available area per breeding pair), but no evidence of density effects at patch level (breeding pairs per hectare). Birds experiencing low territory level density throughout their life bred on average earlier, and this response was also seen at the within-individual level (earlier laying in years when individuals experienced a lower density than average). We found no context-dependence of plastic responses to warmer springs as we detected no interactive effects between density and temperature. In terms of breeding success, earlier laying decreased the risk of brood failure and increased the number of fledglings. The number of fledglings was higher at low territory level density, while higher patch level density increased the probability of brood failure. Altogether, these results indicate that density-related responses were likely mediated by food competition rather than by increased numbers of low-quality birds or increased occupation of poorer territories at higher densities. This study highlights the importance of examining parameters at different spatial scales, along with the study of individual responses to multivariate cues for a comprehensive understanding of the variations in phenological plasticity.

Keywords: breeding phenology, multidimensional plasticity, population density, territory-size

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www.oikosjournal.org

© 2025 The Author(s). Oikos published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

<footnote n="1"><sup>1</sup>CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France</footnote>

<footnote n="2"><sup>2</sup>University of Antwerp, Campus Drie Eiken, Wilrijk, Antwerpen, Belgium</footnote>
**Introduction**

Phenotypic plasticity, the ability of a single individual (or genotype) to alter its phenotype in response to cues in the environment, represents an important adaptive mechanism (Dewitt and Scheiner 2004, Nussey et al. 2007). Given its potential to facilitate swift adjustments to changing environmental conditions, plasticity has gained considerable attention in the context of ongoing climate change (Gienapp et al. 2008, Merilä and Hendry 2014). Phenological traits are of specific concern, notably due to their relevance in trophic interactions (Thackeray et al. 2016). Over the last few decades, numerous studies have documented earlier occurrences of phenological events in response to rising temperatures across various taxa spanning from plants to mammals (Root et al. 2003, Menzel et al. 2006, Parmesan 2006, Thackeray et al. 2010, Polozzánska et al. 2013) and these responses are largely known to be adaptive though not necessarily sufficient to keep track of climate change (Charmantier et al. 2008, Charmantier and Gienapp 2014, Radchuk et al. 2019).

Plasticity of phenology in response to temperature can mean a change in developmental processes, such as faster developmental rates in both plants and insects (Walther et al. 2002), but also changes in labile, repeatedly expressed traits such as flowering or breeding time (Nussey et al. 2007). For interacting species in food webs (e.g. pollinators and predators, Voigt et al. 2003), if the resource species express developmental plasticity in response to temperature, the optimal phenology of other species may be altered, selecting for phenological plasticity. For instance, insectivorous species heavily rely on prey abundance for their reproductive success, and a mismatch between their breeding period and the peak availability of insects can result in detrimental effects on their fitness (Visser and Both 2005, Both et al. 2006, Charmantier et al. 2008). Consequently, animals are known to utilize a combination of cues to anticipate the optimal timing for breeding concerning the availability of their food source (Chmura et al. 2019). Among the most extensively documented responses to temperature in terms of breeding timing is the plasticity observed in passerine birds such as the blue tits *Cyanistes caeruleus* and great tits *Parus major*. Studies have consistently demonstrated that these species adjust their breeding phenology by breeding earlier in response to warmer spring temperatures (Van Noordwijk et al. 1995, Visser et al. 2004, Menzel et al. 2006, Charmantier et al. 2008, Møller et al. 2010, Matthysen et al. 2011, Porlier et al. 2012, Bailey et al. 2022), enabling them to synchronize their reproduction with the peak abundance of caterpillars (Both et al. 2006, Chevin et al. 2015, Bonamour et al. 2020).

However, natural environments are composed of a multitude of environmental factors beyond temperature that can also affect the expression of plasticity. Although our understanding of how a single environmental gradient such as temperature could influence plastic responses in phenotypic traits has been well established, knowledge on how multiple environmental gradients influence the expression of plasticity is still limited (Westneat et al. 2019). In the wild, depending

on whether phenotypic optima are similar or differ under different combinations of environmental factors, adaptive responses to each environmental factor may be aligned or antagonistic (Chevin and Lande 2015, Snell-Rood and Kobiela 2020). For example, a tradeoff may arise between a costly adaptive response to a particular environmental gradient and the maintenance of other functions in a stressful environment (DeWitt et al. 1998, Callahan et al. 2008). Limited number of studies investigating the combined effects of density and temperature on breeding time at an individual level either report additive responses (Wilson et al. 2007) or indicate a stronger response to temperature at high density (Bourret et al. 2015), or no effect of breeding population density on laying dates (Burant et al. 2022). The adaptive response to multiple factors may thus be an additive or non-additive combination of the responses to single factors, making it important to investigate these interactions and how they affect fitness in the wild.

Density of conspecifics is an important driver of competition for resources that can vary temporally and spatially and operate at different spatial scales as well (Dhondt et al. 1992, Dhondt 2012). Density not only influences individual performances such as breeding success and survival (Dunn et al. 2015), but also breeding phenology and its plasticity (Wilkin et al. 2006, Wilson et al. 2007, Møller et al. 2020). However, population density can have different meanings and implications for breeding time and its plasticity, because density may not only reflect competition but can also correlate with the demographic composition of the population in terms of individual quality as well as territory quality (Forsman et al. 2008, Buxton et al. 2020). Density has been suggested to delay breeding time through three mechanisms: a higher proportion of poor-quality breeders (Fretwell and Lucas 1969, Lessard et al. 2014), higher occupancy of poor-quality territories (Dhondt et al. 1992, 2010), and/or a lower availability of food due to competition (Dhondt 2010). Through its effects on condition, high density could thus also constrain plasticity of laying dates if birds are unable to pay the costs of breeding earlier (Nilsson and Råberg 2001, Vézina and Williams 2002). For example, in the song sparrow *Melospiza melodia*, females born in years of higher density displayed less plasticity in response to the Southern Oscillation Index (SOI) (Wilson et al. 2007). However, when density mostly reflects high level of competition for food, earlier breeding in response to higher density may allow individuals to escape competition (Nilsson et al. 2019, Møller et al. 2020) by either capturing the resources for their offspring before their competitors and/or by increasing synchronisation with the food peak (birds are usually late with respect to the optimal laying date, de Villemereuil et al. 2020). In line with this, selection for earlier breeding has been shown to be stronger at high density in a Finnish population of pied flycatchers (Ahola et al. 2012). In this context, plasticity of laying date in response to temperature could be expected to be steeper at higher density, as has already been found in a study across blue and great tit populations (Møller et al. 2020).
Density has been investigated at different spatial scales, from overall population densities (Wilson et al. 2007), or patch-level densities within a larger population (Bourret et al. 2015, Møller et al. 2020) to the scale of individual territories (Wilkin et al. 2006). These different proxies of density may represent different biological processes. Density when considered at a more global or population scale, such as patch level density during the breeding period, may reflect competition at the population level but can also include information about the harshness of the previous winter or recruitment success (Forsman et al. 2008, Buxton et al. 2020). In turn, density when considered at the scale of individual territories may be more informative about the extent of competition at local level. Hence, investigating the effects of density at both territory (local) and patch (global) levels on breeding time and breeding success may allow a better understanding of the processes at play. It should be noted that breeding decisions and breeding outcome may also be affected by interspecific competition as has already been evidenced in the blue tit–great tit species pair (Dhondt 2012, Stenseth et al. 2015, Møller et al. 2020), although effects of interspecific competition are generally expected to be weaker than intraspecific effects (Gurevitch et al. 1992, Mutshinda et al. 2009, Møller et al. 2020).

In this study we aim to investigate the effects of population density on phenotypic plasticity in egg-laying date, utilizing data from a long-term monitoring of a great tit population. We explore variation in temperature-induced plasticity in response to breeder density at both territory (local scale) and patch levels (global scale). We also aim to understand the additive or interactive nature of these factors on fitness, to interpret the adaptive/non-adaptive nature of responses to multiple environmental factors (Table 1). Furthermore, we used an individual mean centring approach to separate individual responses to density variation from effects of changing population composition (i.e. within- versus among-individual components of variation), and this both with respect

to individual and territory identities (Dingemanse and Dochtermann 2013). Overall, if density responses are not part of an adaptive response, we expect later breeding, shallower plasticity, and lower breeding success at high density (Table 1). If these effects are mediated by a higher proportion of poor-quality birds or by an increased use of low-quality territories, we expect to mostly detect differences among rather than within individuals or territories, respectively. In this scenario, patch level density effects should be stronger than territory level effects. If density effects are mediated by lower food abundance for all individuals, however, we expect a prevalence of within-individual responses and stronger territory level than patch level effects. In turn, if responses to density are adaptive, earlier laying and steeper plasticity should lead to similar breeding success across densities, with strong within-individual effects (Table 1). Finally, we checked whether our outcomes changed by including inter-specific competition with blue tits in our density estimates.

## Material and methods

### Field site

This study uses data from a long-term great and blue tit monitoring program in Belgium from 1994 to 2016. The 'Boshoek' field site (51°08'N, 04°31'E) encompasses 12 patches of fragmented woodlands (1–15 ha) in an approximately 10 km² area near Antwerp. For the purpose of this study, data from the four largest oak patches – ZZ, (11.1 ha with 96 boxes); KB, (10.1 ha with 90 boxes); LO, (6.2 ha with 62 boxes); and HN (2.8 ha with 23 boxes) – were used due to their comparable habitat characteristics (Matthysen 2002). In all sites approximately nine boxes were available per hectare, whereby two thirds of boxes had a 32 mm opening (allowing both blue and great tits) and one third had a 26 mm opening (allowing blue tits only). These patches were

<table><caption>Table 1. Expected effects of population density on breeding time and reproductive success at territory (local) and patch level (global) scale. Here we refer to within-individual response as the plastic response a given individual may express to density regardless of the territory in which it breeds, while within-territory effects refer to the variation in laying date in response to density expressed by all birds breeding in a given territory. Also note that site fidelity and a correlation between individual quality and territory quality could slightly shift these predictions.</caption><thead><tr><th rowspan="2"></th><th colspan="4">Hypothesis on the origin of density effects</th></tr><tr><th>Food competition as constraint</th><th>Food competition – adaptive response</th><th>More birds of lower quality</th><th>Territories of lower quality</th></tr></thead><tbody><tr><td>Mechanism</td><td>Birds are in poorer condition at high densities</td><td>Birds anticipate stronger competition when breeding</td><td>More low-quality birds in the population</td><td>More low-quality territories occupied</td></tr><tr><td>Density effect on breeding time</td><td>Breed later at high densities</td><td>Breed earlier at high densities</td><td>Breed later at high densities</td><td>Breed later at high densities</td></tr><tr><td>Density effect on plasticity</td><td>Shallower slope at high densities</td><td>Steeper slope at high densities</td><td>Shallower slope at high densities</td><td>Shallower slope at high densities</td></tr><tr><td>Spatial scale of density effects</td><td>Stronger at territory level</td><td>At both levels</td><td>Stronger at patch-level</td><td>Stronger at patch-level</td></tr><tr><td>Within-individual response</td><td>Strong</td><td>Strong</td><td>Weak</td><td>Intermediate</td></tr><tr><td>Within-territory effects</td><td>Strong</td><td>Strong</td><td>Strong</td><td>Weak</td></tr><tr><td>Density effect on breeding success</td><td>Lower at high density</td><td>Density × Laydate interaction</td><td>Lower at high density</td><td>Lower at high density</td></tr></tbody></table>
characterised predominantly by mature stands of common oak *Quercus robur*, undergrowth of hazel *Coryllus avellana*, and black cherry *Prunus serotina*, and had distinct boundaries well-defined by the sharp transition from closed-canopy forest to open areas such as fields or meadows, residential areas, or small farms (Matthysen 2002). During four years of the study, a density manipulation experiment was conducted in patches KB and LO whereby the total number of boxes was held constant, but half of the boxes from the eastern half were moved to the western half for two years (2000 and 2001). This treatment was reversed in 2003 and 2004. Data from these years are included in our analyses; however, we have not analysed the density manipulation experiments separately since these ran over four years with limited variation in temperature, insufficient to detect any interaction effects of temperature and density on egg-laying date. Moreover, the density variation created by these manipulations remained close to the naturally observed range.

## Breeding data

Basic phenological data (Matthysen et al. 2011) were collected by conducting weekly nest box checks before egg laying. First-egg dates were estimated from the first observation of a partially laid clutch, as one egg is usually laid per day (average clutch size = 8.7). Parents were captured at the nest when the chicks were eight days old, ringed, and aged (first-years versus adults [≥ 2 years]) based on plumage characteristics. Chicks were counted and ringed on day 15 post-hatching and the number of fledged young was recorded after checking for dead nestlings after the nest fledged. We have used data from first clutches only in all our models.

## Environmental parameters

We used two proxies to define density of birds at a local scale or territory level: 1) the tessellated area around the nest box and 2) distance to the nearest neighbour (nearest occupied nest box). Voronoi tessellation technique has been used to form polygons representing virtual territories (Tanemura and Hasegawa 1980, Stoyan et al. 1995, Wilkin et al. 2006, 2007) around occupied nest boxes. A tessellated polygonal area or 'territory size' (henceforth in this document) was calculated around each nest box by placing boundaries exactly midway between all adjacent neighbours (Pebesma and Bivand 2023). This is a proxy for the area available to each breeding pair, and since it is fully determined by the proximity to surrounding neighbours, it also reflects population density (birds per area) at the level of the breeding pair and its immediate surroundings. To ensure nest boxes on the edge of the sites did not have unrealistically large areas due to the lack of neighbours on the edges of the patch, the tessellated areas were restricted to the woodland perimeter, resulting in a maximum territory size of 10 871 m² (Fig. 1A–B). Considering that nest boxes were occupied by both great tits and blue tits, we first ran the tessellation on the whole patch with nest boxes occupied by great tits (GT) only, and a second time including nest boxes

occupied by either great or blue tits (BT) (Supporting information). In addition, to estimate patch level density, we considered the number of occupied nest boxes per hectare, in each of the four patches per year (Fig 1A). Note that because of the four patches, we obtained four density estimates per year. In total, we devised six density proxies: three including only great tits and then three including both blue and great tits.

For temperature cues predicting egg-laying dates, we used the temperature window previously determined for this population by Bailey et al. (2022) and available from their supplementary material (https://doi.org/10.5281/zenodo.5747635). Briefly, they used a sliding window analysis (see their paper for details) evaluating the window of temperature best explaining population average laying date. Daily temperatures data were obtained from E-OBS Gridded Dataset ver. 17.0 (European Climate Assessment & Dataset project (ECA&D; https://www.ecad.eu). They determined that laying dates were best explained by the average temperature between 1 March and 7 April.

## Statistical analyses

### Laying date

To study the effect of density proxies and temperature on egg-laying date, we used linear mixed models including each of the density proxies, temperature, and age of females as fixed effects. The effect of density at territory level and patch level were investigated by fitting separate models. Note that the correlation between the two density proxies was low at -0.35, 95% CI [-0.39, -0.31]. We investigated all two-way interactions, with a special interest for the interactions between temperature and density proxies. Three-way interactions were not investigated. Female identity, year, and study plot were fitted as random intercepts. Due to the lack of power we could not integrate individual random slopes (Van De Pol 2012) for individual responses to annual patch level densities and annual spring temperatures in our models. For territory level density, results were not affected by the inclusion of random slope (results not shown). We thus only present models including random intercepts. We found no evidence of non-linear relationships between laying date and our environmental variables (GAMs, see the Supporting information).

To assess whether the effects of density on egg-laying date were due to plastic individual responses or to differences in the average environment individuals may have experienced, we separated the within- and among-individual components for each of our density proxies using individual mean centring (Van De Pol and Wright 2009). To that purpose, instead of using the annual estimate of density as in previous models, here the individual's environments were described by both the average environment individuals experience throughout their breeding seasons (mean density) and by the annual deviation from this average (annual density–average density). Both descriptors (average and deviation) were included in the same model, and we tested their interaction with temperature. This was run for both density proxies. Since variation
<figure><img src="image_26.png" /><figcaption>Figure 1. Representation of territory level and patch level density proxies of the Boshoek site (example for the year 2011). (A) Density at patch level was estimated as the total number of occupied next boxes per hectare in each patch (HN, ZZ, KB and LO). Each yellow point in a patch represents a great tit occupied nestbox. (B) Density at territory level was evaluated through territory size around each nest box (orange circles, here in patch 'KB', n.b. larger territory size means lower density) for great tits (GT) only. The colour scale differentiates patches from low to high density (A) and territories from smallest to largest in territory size (B).</figcaption></figure>

in territory level density may be associated with nest boxes (e.g. because of differences in average environment or habitat in different parts of the patch) rather than with female identity, we repeated the same analysis using nest box as the subject instead of female identity. Note that for territory sizes and neighbour distances a positive estimate implies a decrease rather than an increase with density.

## Breeding success

We investigated the effects of each of our density proxies and egg-laying date on breeding success by fitting separate generalized mixed models with hurdle distribution as they allowed us to simultaneously model the probability of brood failure with a binomial distribution and the factors affecting the number of fledged young with a zero-truncated Poisson
process. We included year and patch as random intercepts for all the models. Since the effect of bird identity was very small, and prevented the model from converging, it was removed from the analyses. Separate analyses were run investigating the effects of within- and among-individual components of each of the density proxies on breeding success. These models also included egg-laying dates and female age as main predictors, and the year and patch as random intercepts. We investigated all two-way interactions, particularly between egg-laying date and density proxies, but three-way interactions were not investigated.

All our predictor variables for the models were scaled by standard deviation and mean centred using the *scale* function in R. Statistical analyses were performed implementing linear mixed models (LMM), using the package 'lme4' (Bates et al. 2014) and generalized linear mixed models (GLMM) with the 'glmmTMB' package (Brooks et al. 2017) in R, ver. 4.2.3

(RStudio, http://www.rstudio.com). For each model, we used visual inspection to ensure assumptions of normality of residuals were met.

## Results

### Plasticity in egg-laying date

We found an additive effect of territory level density and temperature, showing that individuals bred earlier in larger territories (i.e. at lower density) and in warmer springs but there was no interaction between territory level density and temperature on laying date (Fig. 2, Table 2A–B). Older females bred earlier than one-year old females (Table 2A–B). Differences in egg-laying dates were explained by both among- and within-individual variation in territory level

<figure><img src="image_27.png" /><figcaption>Figure 2. Effect of spring temperature and density on laying dates of great tits. Dots represent individual (raw) data points of laying date according to spring temperature with jitter to reduce overlap. Regression lines represent model predictions including the interaction of density and temperature, the high (0.95) and low (0.05) quantiles for each density proxy. (A) Effects of territory size (note that large territory sizes correspond to low territory level density). (B) Effects of patch level density (no. of occupied nest boxes per hectare in each patch and year).</figcaption></figure>
<table><caption>Table 2. (A–B) Effects of territory level density and temperature using territory size as a proxy for density at local scale on egg-laying date in great tits (LMM, N<sub>Obs</sub>=2176, N<sub>females</sub>=1231, N<sub>Years</sub>=23; for age, the reference level is one-year old females). (A) Analysis based on territory size as territory level density proxy. (B) Analysis splitting territory size into among and within individual components. Significant terms are indicated in bold text.</caption><thead><tr><th>Parameters</th><th>Estimate</th><th>SE</th><th>χ<sup>2</sup></th><th>Pr(&gt; χ<sup>2</sup>)</th></tr></thead><tbody><tr><td><strong>(A)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>(Intercept)</td><td><strong>103.25</strong></td><td>0.35</td><td>87422.57</td><td>&lt; 0.0001</td></tr><tr><td>Spring temperature</td><td><strong>-5.14</strong></td><td>0.32</td><td>258.14</td><td>&lt; 0.0001</td></tr><tr><td>Female age (older)</td><td><strong>-1.91</strong></td><td>0.18</td><td>108.78</td><td>&lt; 0.0001</td></tr><tr><td>Territory size</td><td><strong>-0.45</strong></td><td>0.10</td><td>19.10</td><td>0.00</td></tr><tr><td>Territory size × Temperature</td><td>-0.11</td><td>0.09</td><td>1.59</td><td>0.21</td></tr><tr><td><strong>(B)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>(Intercept)</td><td><strong>103.40</strong></td><td>0.49</td><td>45425.86</td><td>&lt; 0.0001</td></tr><tr><td>Spring temperature</td><td><strong>-5.13</strong></td><td>0.32</td><td>257.32</td><td>&lt; 0.0001</td></tr><tr><td>Female age (Older)</td><td><strong>-1.93</strong></td><td>0.18</td><td>110.81</td><td>&lt; 0.0001</td></tr><tr><td>Among-individual</td><td><strong>-0.34</strong></td><td>0.12</td><td>7.37</td><td>0.01</td></tr><tr><td>Within-individual</td><td><strong>-0.23</strong></td><td>0.08</td><td>9.16</td><td>0.00</td></tr><tr><td>Among-individual × Temperature</td><td>-0.16</td><td>0.09</td><td>3.26</td><td>0.07</td></tr><tr><td>Within-individual × Temperature</td><td>0.00</td><td>0.10</td><td>0.00</td><td>0.97</td></tr></tbody></table>

density (Table 2B): females having larger territories on average bred earlier throughout their life, and females also bred earlier in years when they had larger territories. We found qualitatively similar outcomes for effects of neighbour distances (Supporting information). Upon repeating the same set of analyses for territory level density taking both blue and great tits into account, we found qualitatively the same effect of territory level density on egg-laying (Supporting information). However, differences in egg-laying dates were now only explained by the within-individual variation in territory level density (Supporting information). Further, when investigating the effect of territory size variation within and across nest boxes for great tits only (i.e. replacing female identity with nest box identity), we found that birds did not breed earlier in nest boxes that had consistently larger territories, but did breed earlier in a given nest box when the surrounding territory was relatively large (Table 3).

When considering density at patch level, we found no significant main effect nor an interactive effect with spring temperature on egg-laying date (Table 4A, Fig 2B). However, there was a negative interaction between temperature and within-individual variation in density at patch level, showing that females responded more strongly to temperature in years when they experienced higher density at patch level (Table 4B). There was no effect of among-individual variation in patch level density on egg-laying dates, nor an interaction with temperature. When investigating the effect of patch density of blue and great tits combined, we found qualitatively the same results with slightly smaller effect sizes (Supporting information).

## Effect of density and egg-laying date on fledging success

We found lower breeding success in higher-density environments when investigating effects of density at both territory and patch levels. The probability of brood failure was not significantly related to territory level density, but it increased

when birds bred later (Fig. 3C) and if they were older (as evidenced in the zero inflated component of the hurdle model; Fig. 3, Table 5A–B). Birds were likely to have more fledglings when territories were larger, when they laid earlier, and when spring temperatures were lower (based on the Poisson component of the hurdle model; Fig. 3A–B, Table 5A). We found no significant interactions between territory level density, laying date, or age of females on the number of fledged young or the likelihood of brood failure (Table 5B). Females having larger territories on average across years fledged a higher number of young (Table 5B) while within-female variation in territory level density had no effect. When investigating the effect of territory level density with blue and great tits combined, we found no main effect of territory level density nor an interactive effect of territory level density and laying date or territory level density and age, either on the number of fledged young or the probability of brood failure (Supporting information). Further, there was no effect of territory level density when split into among and within-individual components on the number of fledged young or the probability of brood failure (Supporting information).

When considering patch level density, we found that the probability of brood failure significantly increased when density was high (Table 6A, Fig. 4C) but there was no main effect of patch density on the number of fledged young. We found no interactive effects of patch density and laying date,

<table><caption>Table 3. Effects of within and among nest box variation on laying date in great tits (LMM, N<sub>Obs</sub>=2176, N<sub>females</sub>=1231, N<sub>Years</sub>=23). Significant terms are indicated in bold text.</caption><thead><tr><th>Parameters</th><th>Estimate</th><th>SE</th><th>χ<sup>2</sup></th><th>Pr(&gt; χ<sup>2</sup>)</th></tr></thead><tbody><tr><td>(Intercept)</td><td><strong>103.39</strong></td><td>0.50</td><td>43091.45</td><td>&lt; 0.0001</td></tr><tr><td>Spring temperature</td><td><strong>-5.14</strong></td><td>0.32</td><td>258.57</td><td>&lt; 0.0001</td></tr><tr><td>Female age (older)</td><td><strong>-1.93</strong></td><td>0.18</td><td>110.90</td><td>&lt; 0.0001</td></tr><tr><td>Among-nest box</td><td>0.04</td><td>0.11</td><td>0.11</td><td>0.74</td></tr><tr><td>Within-nest box</td><td><strong>-0.23</strong></td><td>0.08</td><td>8.42</td><td>0.00</td></tr></tbody></table>
<table><caption>Table 4. (A–B) Effects of patch level density and temperature on great tit laying dates (LMM, N<sub>Obs</sub>=2179, N<sub>females</sub>=1234, N<sub>years</sub>=23; for age, the reference level is one-year old females). (A) Analysis based on density at patch level. (B) Analysis splitting patch level density into among and within individual components. Significant terms are indicated in bold text.</caption><thead><tr><th>Parameters</th><th>Estimate</th><th>SE</th><th>χ<sup>2</sup></th><th>Pr(&gt; χ<sup>2</sup>)</th></tr></thead><tbody><tr><td><strong>(A)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>(Intercept)</td><td><strong>103.40</strong></td><td>0.49</td><td>45251.08</td><td>&lt; 0.0001</td></tr><tr><td>Spring temperature</td><td><strong>-5.14</strong></td><td>0.32</td><td>259.19</td><td>&lt; 0.0001</td></tr><tr><td>Female age (older)</td><td><strong>-1.94</strong></td><td>0.18</td><td>111.90</td><td>&lt; 0.0001</td></tr><tr><td>Patch density</td><td>0.00</td><td>0.17</td><td>0.00</td><td>0.98</td></tr><tr><td>Patch density × Temperature</td><td>-0.18</td><td>0.11</td><td>3.00</td><td>0.08</td></tr><tr><td><strong>(B)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>(Intercept)</td><td><strong>103.39</strong></td><td>0.48</td><td>45823.06</td><td>&lt; 0.0001</td></tr><tr><td>Spring temperature</td><td><strong>-5.15</strong></td><td>0.32</td><td>264.69</td><td>&lt; 0.0001</td></tr><tr><td>Female age (older)</td><td><strong>-1.93</strong></td><td>0.18</td><td>110.05</td><td>&lt; 0.0001</td></tr><tr><td>Among-individual</td><td>-0.11</td><td>0.19</td><td>0.34</td><td>0.56</td></tr><tr><td>Within-individual</td><td>0.04</td><td>0.10</td><td>0.20</td><td>0.66</td></tr><tr><td>Among-individual × Temperature</td><td>-0.10</td><td>0.10</td><td>0.89</td><td>0.35</td></tr><tr><td>Within-individual × Temperature</td><td><strong>-0.22</strong></td><td>0.10</td><td>5.10</td><td>0.02</td></tr></tbody></table>

or patch density and age of females, on the number of fledged young or the probability of failure (Table 6B). Further, the likelihood of brood failure was explained only by the among-individual variation in patch density (Table 6B) showing higher likelihood of brood mortality for birds that on average experienced high patch level densities (Table 6B).

Discussion

In this study, we report additive effects of spring temperature and density on laying date, demonstrating earlier breeding in warmer springs and at low territory-level densities. Responses to territory level density were largely due to individual

<figure><img src="image_28.png" /><figcaption>Figure 3. Effect of territory level density, laying date, and spring temperature on breeding success in great tits. Regression lines represent predictions from hurdle model with the high (0.95) and low (0.05) quantiles for territory size (larger available area denotes lower territory level density). (A) Effect of spring temperature and territory size on the number of fledged young. (B) Effect of laying date and territory size on number of fledged young. (C) Effect of laying date and territory size on the likelihood of brood failure. Note that in (A), (B), and (C) raw data points have been superimposed over model predictions. In (C) the raw data are in binomial format.</figcaption></figure>
plasticity. In contrast, there were no significant effects of density at the patch level, suggesting that density effects are likely mediated by processes at territory or local level rather than at the population level. Our results support the hypothesis that density effects on laying date are primarily a non-adaptive response mediated by competition for food. We found that higher density reduced breeding success but had no effect on the expression of adaptive plasticity to temperature.

Several lines of evidence support the predictions from the food limitation hypothesis rather than the hypotheses on increased use of low-quality habitat or increased proportion of low-quality birds (Table 1). We show that birds began breeding later at higher density, but this was only observed at the territory level (local scale) where food competition is occurring in great tits during the breeding season (Krebs 1982, Dhondt 2012), rather than at the patch level (global scale). These responses were detected across individuals but also within individuals as birds bred earlier in years when they had larger territories. These results support the existence of within-individual plasticity and thus argue against the hypothesis of density effects being mediated by an increased number of low-quality birds at higher densities (Table 1). The observed within-individual plasticity in response to

<table><caption>Table 5. (A–B) Effects of territory level density using territory size as proxy for density at local scale, laying date, and temperature on fledging success in great tits (N<sub>Obs</sub>=2174, years=23; for age, the reference level is one-year old females). (A) Analysis based on territory size as territory level density proxy. (B) Analysis splitting territory size into among and within individual components. Significant terms are indicated in bold text.</caption><thead><tr><th>Parameters</th><th>Estimate</th><th>SE</th><th>z value</th><th>Pr(&gt;|z|)</th></tr></thead><tbody><tr><td><strong>(A)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Number of fledglings (Intercept)</td><td><strong>2.08</strong></td><td>0.03</td><td>81.71</td><td>&lt; 0.0001</td></tr><tr><td>Territory size</td><td><strong>0.02</strong></td><td>0.01</td><td>2.38</td><td>0.02</td></tr><tr><td>Egg-laying date</td><td><strong>-0.07</strong></td><td>0.01</td><td>-5.78</td><td>0.001</td></tr><tr><td>Female age (older)</td><td>0.02</td><td>0.02</td><td>1.3</td><td>0.19</td></tr><tr><td>Spring temperature</td><td><strong>-0.04</strong></td><td>0.02</td><td>-2.44</td><td>0.01</td></tr><tr><td>Likelihood of failure (Intercept)</td><td><strong>-2.82</strong></td><td>0.16</td><td>-17.24</td><td>&lt; 0.0001</td></tr><tr><td>Territory size</td><td>0.02</td><td>0.09</td><td>0.22</td><td>0.83</td></tr><tr><td>Egg-laying date</td><td><strong>0.28</strong></td><td>0.12</td><td>2.41</td><td>0.02</td></tr><tr><td>Female age (older)</td><td><strong>0.45</strong></td><td>0.17</td><td>2.66</td><td>0.01</td></tr><tr><td>Spring temperature</td><td>0.06</td><td>0.14</td><td>0.41</td><td>0.68</td></tr><tr><td><strong>(B)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Number of fledglings (Intercept)</td><td><strong>2.08</strong></td><td>0.03</td><td>80.86</td><td>&lt; 0.0001</td></tr><tr><td>Among-individual</td><td><strong>0.02</strong></td><td>0.01</td><td>2.76</td><td>0.01</td></tr><tr><td>Within-individual</td><td>0.00</td><td>0.01</td><td>0.26</td><td>0.8</td></tr><tr><td>Egg-laying date</td><td><strong>-0.07</strong></td><td>0.01</td><td>-5.77</td><td>0.001</td></tr><tr><td>Female age (older)</td><td>0.02</td><td>0.02</td><td>1.33</td><td>0.18</td></tr><tr><td>Spring temperature</td><td><strong>-0.04</strong></td><td>0.02</td><td>-2.46</td><td>0.01</td></tr><tr><td>Likelihood of failure (Intercept)</td><td><strong>-2.82</strong></td><td>0.16</td><td>-17.22</td><td>&lt; 0.0001</td></tr><tr><td>Among-individual</td><td>0.01</td><td>0.09</td><td>0.17</td><td>0.87</td></tr><tr><td>Within-individual</td><td>0.01</td><td>0.08</td><td>0.15</td><td>0.88</td></tr><tr><td>Egg-laying date</td><td><strong>0.28</strong></td><td>0.12</td><td>2.41</td><td>0.02</td></tr><tr><td>Female age (older)</td><td><strong>0.45</strong></td><td>0.17</td><td>2.66</td><td>0.01</td></tr><tr><td>Spring temperature</td><td>0.06</td><td>0.14</td><td>0.41</td><td>0.68</td></tr></tbody></table>

density indicates that individual birds adjust their breeding time based on their energetic states driven by food availability rather than by broader population-level processes. Laying has a strong energetic cost (Williams 2005) and earlier breeding when food is abundant is consistent with observational (Nager and van Noordwijk 1995) and experimental data in great tits (von Bromssen and Janssen 1980, Nilsson and Svensson 1993, Svensson and Nilsson 1995, Nager et al. 1997), and in other species (reviewed in Robb et al. 2008, Dhondt 2012).

Note that we also detected between-individual variation with earlier laying in individuals that on average had larger territories, but this result should be treated with caution as many individuals in our dataset only experienced one year of breeding (62%). For these individuals, the within-individual component of the response cannot be evaluated, so they may contribute to inflate the part of the response attributed to between-individual differences. We also found that differences in laying dates were solely accounted for by the annual variations in territory size around nest boxes, but not by the average territory size for a given nest box. Therefore, changes in laying date with territory level density do not seem to be driven by the quality of the territory, strengthening the conclusion that density effects do not arise from the increased occupation of lower-quality territories. This is consistent

<table><caption>Table 6. (A–B) Effects of patch level density and egg-laying date on fledging success in great tits (N<sub>Obs</sub>=2174, years=23; for age, the reference level is one-year old females). (A) Analysis based on patch density. (B) Analysis splitting patch density into among and within individual components. Significant terms are indicated in bold text.</caption><thead><tr><th>Parameters</th><th>Estimate</th><th>SE</th><th>z value</th><th>Pr(&gt;χ<sup>2</sup>)</th></tr></thead><tbody><tr><td><strong>(A)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Number of fledglings (Intercept)</td><td><strong>2.08</strong></td><td>0.02</td><td>83.65</td><td>&lt; 0.0001</td></tr><tr><td>Patch density</td><td>0.00</td><td>0.01</td><td>-0.24</td><td>0.81</td></tr><tr><td>Laying date</td><td><strong>-0.07</strong></td><td>0.01</td><td>-6.04</td><td>0.00</td></tr><tr><td>Female age (older)</td><td>0.02</td><td>0.02</td><td>1.21</td><td>0.23</td></tr><tr><td>Spring temperature</td><td><strong>-0.04</strong></td><td>0.02</td><td>-2.60</td><td>0.01</td></tr><tr><td>Likelihood of failure (Intercept)</td><td><strong>-2.83</strong></td><td>0.15</td><td>-18.95</td><td>&lt; 0.0001</td></tr><tr><td>Patch density</td><td><strong>0.24</strong></td><td>0.09</td><td>2.75</td><td>0.01</td></tr><tr><td>Laying date</td><td><strong>0.28</strong></td><td>0.12</td><td>2.40</td><td>0.02</td></tr><tr><td>Female age (older)</td><td><strong>0.47</strong></td><td>0.17</td><td>2.78</td><td>0.01</td></tr><tr><td>Spring temperature</td><td>0.05</td><td>0.13</td><td>0.39</td><td>0.70</td></tr><tr><td><strong>(B)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Number of fledglings (Intercept)</td><td><strong>2.08</strong></td><td>0.02</td><td>83.63</td><td>&lt; 0.0001</td></tr><tr><td>Spring temperature</td><td><strong>-0.04</strong></td><td>0.02</td><td>-2.60</td><td>0.01</td></tr><tr><td>Female age (older)</td><td>0.02</td><td>0.02</td><td>1.21</td><td>0.23</td></tr><tr><td>Laying date</td><td><strong>-0.07</strong></td><td>0.01</td><td>-6.04</td><td>0.00</td></tr><tr><td>Among-individual</td><td>0.00</td><td>0.01</td><td>-0.19</td><td>0.85</td></tr><tr><td>Within-individual</td><td>0.00</td><td>0.01</td><td>-0.19</td><td>0.85</td></tr><tr><td>Likelihood of failure (Intercept)</td><td><strong>-2.84</strong></td><td>0.15</td><td>-18.91</td><td>&lt; 0.0001</td></tr><tr><td>Spring temperature</td><td>0.07</td><td>0.13</td><td>0.53</td><td>0.60</td></tr><tr><td>Female age (older)</td><td><strong>0.48</strong></td><td>0.17</td><td>2.80</td><td>0.01</td></tr><tr><td>Laying date</td><td><strong>0.28</strong></td><td>0.12</td><td>2.45</td><td>0.01</td></tr><tr><td>Among-individual</td><td><strong>0.29</strong></td><td>0.09</td><td>3.25</td><td>0.00</td></tr><tr><td>Within-individual</td><td>-0.01</td><td>0.08</td><td>-0.07</td><td>0.94</td></tr></tbody></table>
<figure><img src="image_29.png" /><figcaption>Figure 4. Effect of patch level density, laying date, and spring temperature on breeding success in great tits. Regression lines represent predictions from hurdle model with the high (0.95) and low (0.05) quantiles for patch density. (A) Effect of spring temperature and patch density on the number of fledged young. (B) Effect of laying date and patch level density on number of fledged young. (C) Effect of laying date and patch level density on the likelihood of brood failure. Note that in (A), (B) and (C) raw data points have been superimposed over model predictions. In (C) the raw data are in binomial format.</figcaption></figure>

with the different patches monitored in this study representing rather homogeneous and largely monospecific stands of oak trees.

Our results are consistent with two studies showing earlier
breeding at low territory level density in great tits (Wilkin et al.
2006) and with lower neighbour distances in blue tits
(Serrano et al. 2017). Wilkin et al. (2006) also showed pheno-
logical plasticity at the individual level, and reduced clutch size
and fledging mass in smaller territories, suggesting a response
to food abundance. They also investigated the scale of density
best explaining breeding time and found that scales smaller
than 1 ha best predicted laying date and found no effect of
density at population level. This is in line with the range of
our estimates of territory level density (territory sizes < 1 ha)
and the absence of patch level density effect on breeding time
in the Boshoek population reported here. The absence of den-
sity effects at patch level seems to be common in great tits,
as this was reported in at least four other temperate popula-
tions (individual plasticity: Wytham Woods: Wilkin et al.
2006; Lauwersmeer area: Nicolaus et al. 2009; Liesbos area:
Both 1998; population averages: Bodey et al. 2021 in Treswell
Wood). Several studies support earlier breeding in great tits
when the conspecific density increases at population level;
however, they are all based on population averages of breeding

time, not on individual responses. The latter has been reported
in a Finnish population (Askainen population: Ahola et al.
2009) but also in two studies across Europe (Møller et al.
2018, 2020) and a study on blue tits in another Belgian popu-
lation (Dhondt 2012).

However, investigating average breeding time does not
allow the assessment of individual plasticity and may inte-
grate the effects of other factors such as a change in the age
composition of the population. A direct comparison is thus
not possible. For example, Bourret et al. (2015) found in the
tree swallows *Tachycineta bicolor* earlier average laying date at
higher patch level density, but no individual plastic response
to density itself. Moreover, the studies by Møller et al. (2018,
2020) incorporated both intraspecific and interspecific den-
sity as well as their interaction with other factors such as
winter temperature, which makes results even less compa-
rable. The existence of such interactions highlights that the
mechanisms at play may be very diverse across populations,
and require more in-depth investigation. Similarly, compari-
son with studies from other species may help to determine
the major factors determining the main modes of action of
density. For example, a study in the song sparrows *Melospiza
melodia* (Wilson et al. 2007) also support later breeding at
higher density due to food limitation. Effects of density at
Writing – original draft (equal); Writing – review and editing (equal). **Erik Matthysen:** Conceptualization (equal); Data curation (equal); Formal analysis (equal); Funding acquisition (equal); Investigation (equal); Methodology (equal); Project administration (equal); Resources (equal); Software (equal); Supervision (equal); Validation (equal); Writing – original draft (equal); Writing – review and editing (equal).

## Data availability statement

All the bird breeding data used in this project are accessible via the SPIbirds website https://spibirds.org/en and can be utilized via https://nioo.know.nl/en/projects/spi-birds/study/boshoek. Data files, R codes, and supplementary information are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.w9ghx3g0x (Purushotham et al. 2025).

## Supporting information

The Supporting information associated with this article is available with the online version.

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