Humans are transforming natural habitats into managed urban green areas and impervious surfaces at an unprecedented pace. Yet the effects of human presence per se on animal life-history traits are rarely tested. This is particularly true in cities, where human presence is often indissociable from urbanisation itself. The onset of the SARS-CoV-2 outbreak, along with the resulting lockdown restrictions, offered a unique, “natural experiment” context to investigate wildlife responses to a sudden reduction of human activities. We analysed four years of avian breeding data collected in a European capital city to test whether lockdown measures altered nestbox occupancy and life-history traits in terms of egg-laying date, incubation duration, and clutch size in two urban adapters: great tits (Parus major) and blue tits (Cyanistes caeruleus). Lockdown measures, which modulated human presence, did not influence any of the life-history traits investigated. In contrast, tree cover, a distinct ecological attribute of the urban space, was positively associated with clutch size, a key avian life-history, and reproductive trait. This highlights the importance of habitat quality/ inter-year variation over human activity on the reproduction of urban wildlife. We discuss our results in light of other urban wildlife studies carried out during the pandemic, inviting the scientific community to carefully interpret all lockdown-associated shifts in biological traits.

Avian life-history traits data collection: From the end of March, we checked nestboxes weekly to identify those occupied by great tits and blue tits. A nestbox was considered “occupied” when at least one egg was laid on a completed nest. Weekly checks allowed to precisely record the date of the first egg laid (females usually lay 1 egg per day, and the laying date is recorded from the 1st of April, corresponding to the value of 1), incubation duration  (given in days and calculated as: hatch date – first egg laid date – clutch size – 1, though incubation occasionally starts earlier or later than clutch completion in tits) and clutch size (total number of eggs in the nest). Only the first broods were included in the analyses.

Tree-cover measurements: We measured the percentage of tree cover in a 100m radius around each nestbox following Szulkin et al. Briefly, we downloaded the raster-layer Tree Cover Density from Copernicus Land Monitoring Services (https://land.copernicus.eu/ sitemap; Forests/Tree Cover Density); this raster layer is defined as “the vertical projection of tree crowns to a horizontal earth’s surface”. The map of tree cover was generated in 2015 and contained a 20m-pixel resolution layer. After creating a 100m radius buffer around each nestbox, we obtained the averaged value of tree cover (in %) at the nestbox level using the function Zonal Statistics in qGIS.

Statistical analyses: Statistical analyses were performed within the computing environment R (v.3.6.2), and jointly for great and blue tits (but for occupancy tests, where the two species were analysed separately). The parameter “Species” was fitted as a covariate in all the other models in order to directly assess species-specific trait variation. Because there was a significant association between the percentage of Tree cover and Lockdown status (LEA vs. LENA, see Table S2), we analysed these two parameters in separate models as indicated below.

To test associations between avian life-history traits and lockdown restrictions, all analyses were run using Linear Models or Linear Mixed Effects Models: note that the category Site was fitted as a random effect in the model only if the inter-group variance (n = 7 levels) was higher than zero, to control for pseudo-replication issues in occupancy analyses and because of site-related differences in avian fitness. All numeric predictors were mean-centred for clarity of parameters estimates. In addition, all interactions were tested and excluded from the models if not significant.

Note that, to investigate the effect of lockdown on avian life-history traits, we specifically focused on the interaction between Year and lockdown status (LEA - Lockdown Entrance Allowed vs. LENA - Lockdown Entrance Not Allowed sites), the latter explicitly reflecting a lack of outdoors human activity in LENA sites in 2020.  

To model nestbox occupancy, we fitted Generalised Linear Mixed Effects Models (GLMMs) with binomial distribution (“glmer” function in the R-package “lme4” v.1.1-21). A nestbox was considered occupied (1) only if a great tit or a blue tit (analysed separately) was breeding in the nestbox. Nestbox occupancy (0/1) was fitted as binomial-response variable in each model, while the interaction between the two categorical variables Year (four levels: 2017, 2018, 2019 and 2020) and lockdown status were fitted as predictors. For the analysis of nestboxes occupancy, we fitted the interaction between the two categorical variables Year and Lockdown status as explanatory variables.

To model variation in egg Lay date (the egg laying date of a nest where the first egg was laid on the 1st of April would be coded with the value of 1), we log-transformed the response variable because the residuals of the models were not normally distributed. We consequently fitted a Linear Model (LM) with a Gaussian distribution (“lm” function). The same model structure was used to model incubation variation in days, Incubation duration, where we added Lay date as explanatory variable to control for the fact that earlier clutches in the season are characterized by longer incubation periods than those started later in the season. are often larger than those initiated later.

For clutch size, we ran Linear Mixed Effect Models with Gaussian distribution (“lmer” function in R) with a model structure analogous to the one indicated above. Incubation duration -in days- was fitted as response variable while the interaction between Year and Lockdown status, and the continuous-variable Lay date (to control for seasonal differences in each breeding event, as incubation duration decreases later in the season) were fitted as predictors.

To investigate the role of habitat quality on nestbox occupancy and avian life-history traits, we used analogous model structures to those described above, but the parameter Lockdown was replaced by the continuous variable Tree cover (here used as a proxy of optimal natural food resources, e.g. caterpillars, in great tits and blue tits).

Notes on databases:

The excel file DataBTf1720S contains blue tit data. Missing data is indicated as empty cells.

The excel file DataGT1720S contains great tit data. Missing data is indicated as empty cells.

The excel file Occupdata contains data used for occupancy analyses on great tits and blue tits. Missing data is indicated as NA.