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Total provisioning by parent Northern Flickers (sexes combined) in relation to brood size for 3 nestling stages: ( A ) age 5–7 days, ( B ) age 10–13, and ( C ) age 18–21. The best-fit model was linear at Stage 1 ( n 1⁄4 53 nests) and quadratic at Stages 2 ( n 1⁄4 51 nests) and 3 ( n 1⁄4 41 nests). Symbols represent the experimental treatments.
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Brood enlargement experiments have been conducted in several species of birds to investigate how parents of both sexes adjust their investment in the current breeding attempt. We studied parental feeding effort in the Northern Flicker (Colaptes auratus), a species with partially reversed sex roles where males invest more in parental care than femal...
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Context 1
... with these 2 variables included did not fit the data significantly better ( P 1⁄4 0.46). Thus, these 2 variables were excluded from subsequent models. Provisioning rates were normally distributed and the data fit assumptions of homogeneity of variance, so we used linear mixed-effect models (LME) to investigate the fixed effects of brood size, nestling stage, parent body condition, parent age, and the interaction between stage and brood size on provisioning rates from nests where both parents were known to be present. Random effects were year and nest to account for the repeated measures at the nest. However, because we were interested in sex-specific responses and due to interaction between nest stage and brood size (males: F 1⁄4 7.42, P , 0.001; females: F 1⁄4 4.68, P 1⁄4 0.009), we ran separate models for the provisioning of each parent at each stage (Stage 1: n 53, Stage 2: n 1⁄4 51, Stage 3: n 1⁄4 40) with only year included as a random effect (fitted as a random intercept). We evaluated the support for a full model versus models with fewer parameters (including an intercept-only model) by using Akaike’s information criterion corrected for sample size (AIC c ) and AIC c weights ( w i ). We show models with D i values of 6 (following Richards 2005), but consider models with D i value of 2 AIC c to be as plausible as the top-ranked model (Burnham and Anderson 2002). AIC c weights ( w i ) sum to 1 across the model set and indicate the relative likelihood of a model being the best at describing the data (Burnham and Anderson 2002). We further determined the explanatory power of a fixed factor by summing the weights of all models that included the specific factor (Symonds & Moussalli 2011). Because of model uncertainty in the top models, we generated natural model-averaged parameter estimates 6 unconditional standard error (SE) and 95% confidence intervals (CI) and tested whether they overlapped with zero. Finally, to test whether provisioning rates increased linearly with brood size or reached a threshold, we pooled provisioning by males and females and compared the fit of a linear versus quadratic regression model at each of the three stages with ANOVA F -tests (Zuur et al. 2010). We used LME models and model selection techniques (AIC c ) to investigate the factors that best predicted nestling mass at each of the three nestling stages. Brood size was the only main effect for Stages 1 ( n 1⁄4 58 nests) and 2 ( n 1⁄4 53 nests), but in Stage 3 ( n 1⁄4 40 nests) when we could sex nestlings by plumage, we also included sex and an interaction between sex and brood size. Nest of rearing was included as a random effect to account for multiple nestlings within each brood (fitted as a random intercept). Wing chord, as a dependent variable, was assessed at Stage 3 using an LME model with the same fixed and random effects used for nestling mass. Because not all nestlings were measured at exactly the same age and we wanted to pool nestlings in the LME models, we standardized body mass (at Stages 1 and 2) and wing length based on average values for a particular age from the growth curves of control nestlings in Gow et al. (2013a). We did not standardize mass at Stage 3 because mass plateaus during this stage (Gow et al. 2013a). Because of model selection uncertainty, we focused on parameter estimates ( 6 unconditional SE) and unconditional 95% CI for the fixed parameters in the selection of a ‘‘ best ’’ wing chord LME model. Logistic regression was used to determine the odds ratio of at least one nestling dying according to brood size. To test whether clutch sizes may be individually optimized, we compared the chance of nestling mortality and fledging success in manipulated broods to that of control broods of the same size within the natural population collected over 14 yr. Similarly, we compared fledging mass between manipulated and control broods of the same size within the natural population (collected over a span of 7 yr). The mass at fledging of these control nestlings did not differ between years (ANCOVA: F 9,1462 1⁄4 2.01, P 1⁄4 0.12) and neither did the fledging success ( F 9,819 1⁄4 1.85, P 1⁄4 0.11) so the years were similar in environmental conditions. All analyses were conducted in R version 2.15.2 (R Core Development Team 2012). LME models were run using the lme4 package (Bates et al. 2012) and AIC c values, weights, and natural model-averaging of parameter estimates were run using the AICcmodavg package (Mazerolle 2013). Unless otherwise indicated, data are reported as means 6 SE, with statistical significance set at a 0.05. Averaged over brood sizes, treatments, and years, the mean provisioning rate by males ( n 1⁄4 40) was 1.50 6 0.08, 1.72 6 0.06, and 1.39 6 0.17 trips per hr and for females ( n 40) was 1.41 6 0.07, 1.69 6 0.07, and 1.21 6 0.17 trips per hr for Stages 1–3, respectively. The maximum rate of 5–6 trips/hr occured in the largest broods of 10–11 nestlings at the oldest nestling stage (Figure 2). A post hoc test of the total number of provisioning visits to the nest (males and females pooled) varied according to nestling stage (ANOVA: F 2,142 1⁄4 4.41, P 1⁄4 0.01), with an increase between Stages 1 and 2 (Tukey HSD: P 1⁄4 0.03), but not between Stages 2 and 3 ( P 1⁄4 0.98). Feeding rates between partners did not differ at any stage of the nestling period (paired t -test: Stage 1: t 52 1⁄4 0.99, P 1⁄4 0.32; Stage 2: t 50 0.43, P 1⁄4 0.67; Stage 3: t 39 1⁄4 0.32, P 1⁄4 0.75). Except for males at Stage 2, brood size always appeared in the top model for provisioning rates and led to a summed w i of 0.98 (except for males at Stage 2: w i 0.26 and females Stage 1: w i 1⁄4 0.59; Table 1). Parameter estimates ( 6 unconditional SE) indicated that males and females increased provisioning with brood size at all stages (Supplemental Material Table S1A). Body condition also sometimes appeared in the top model with brood size, but the unconditional CI overlapped zero indicating non-significance. The best model for provisioning rate in relation to brood size was a linear increase at Stage 1, a decelerating quadratic curve at Stage 2, and an increasing curve at Stage 3 (Figure 2). Despite the increase in provisioning rates with brood size, per- nestling provisioning rates decreased with brood size (ANOVA: Stage 1: F 1,51 1⁄4 28.27, P , 0.001; Stage 2: F 1,49 71.21, P , 0.001; Stage 3: F 1,39 1⁄4 7.09, P , 0.01). The decline in per-nestling provisioning rates was fairly linear across the range of brood sizes except at Stage 3 (Figure 3). At Stages 1 and 2, nestlings in the smallest broods received about twice as many feedings per hour as those in the largest broods. Within each stage, nestling mass decreased with brood size (Table 2, Supplemental Material Table S1B, Figure 4). The interaction between brood size and nestling sex was the best model at Stage 3 (Table 2; w i 1⁄4 0.75); mass of male nestlings declined more dramatically with increasing brood size compared to that of females (Figure 4). The wing chord of male nestlings at Stage 3 (116 6 0.7 mm, n 126) did not differ significantly from females (116 6 0.6 mm, n 1⁄4 139; Table 2). Standardized to age, wing chord length decreased with increasing brood size (Table 2, Figure 5). According to parameter estimates and uncon- ditioned CI, only brood size and not sex explained variation in wing length at fledging (Supplemental Material Table S1B). Ten of 16 enlarged nests (63%) experienced nestling mortality as did 8 of 12 control nests (66%) and 2 of 13 reduced nests (15%). Of the 41 broods in the experiment that made it through Stage 3, a single nestling died in 10 cases, 2 nestlings died in 6 cases, and 3 nestlings died in 4 broods. The likelihood that at least one nestling died did not differ between control and enlarged nests ( v 21 1⁄4 0.22, P 0.64), however the likelihood of mortality tended to be lower in reduced compared to control nests ( v 21 1⁄4 3.6, P 0.06) and was significantly lower in reduced compared to enlarged broods ( v 21 1⁄4 5.33, P 1⁄4 0.02). The likelihood of mortality increased with brood size (logistic regression: odds ratio 1⁄4 1.58, 95% CI: 1.18–2.28, P 1⁄4 0.005). To see how mortality changed in relation to the degree of manipulation away from the original brood size, we classified nests according to the number of nestlings added or removed. The likelihood that at least one nestling died declined as nestlings were removed ( v 1⁄4 13.5, P 0.009), but did not increase in enlarged broods as more nestlings were added. Although the number of nestlings that died increased with brood size, the number of nestlings that fledged also increased with brood size (ANOVA: brood size effect: F 1,39 120.6, P , 0.001; treatment effect: F 2,38 1⁄4 33.23, P , 0.001; Figure 6). To determine whether clutch size may be individually optimized, we compared the productivity (number of fledglings) of manipulated broods to control broods of the same size from the natural population. There was no difference in fledging success between enlarged broods and the control broods from the natural population (2-way ANOVA: treatment group effect: F 1,491 1⁄4 0.33, P 0.56; brood size effect: F 3,491 1⁄4 12.89, P , 0.001, interaction effect: F 3,491 1⁄4 0.75, P 1⁄4 0.52; Figure 6). Similarly, the number of fledglings from experimentally reduced broods did not differ from the control broods in the natural population (2-way ANOVA: treatment group effect: F 1,510 1⁄4 0.60, P 1⁄4 0.44; brood size effect: F 3,510 252.38, P , 0.001, interaction effect: F 3,510 1⁄4 0.14, P 1⁄4 0.93; Figure 6). In the comparison of nestling mass between enlarged broods and control broods from the natural population, there was a significant interaction between brood size and treatment group (2-way ANOVA: treatment group effect: F 1,1138 1⁄4 0.53, P 1⁄4 0.47, brood size effect: F 4,1138 1⁄4 13.14, P , 0.001, interaction effect: F 4,1138 1⁄4 4.20, P , 0.01). Inspection of the data showed ...
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... AIC c weights ( w i ). We show models with D i values of 6 (following Richards 2005), but consider models with D i value of 2 AIC c to be as plausible as the top-ranked model (Burnham and Anderson 2002). AIC c weights ( w i ) sum to 1 across the model set and indicate the relative likelihood of a model being the best at describing the data (Burnham and Anderson 2002). We further determined the explanatory power of a fixed factor by summing the weights of all models that included the specific factor (Symonds & Moussalli 2011). Because of model uncertainty in the top models, we generated natural model-averaged parameter estimates 6 unconditional standard error (SE) and 95% confidence intervals (CI) and tested whether they overlapped with zero. Finally, to test whether provisioning rates increased linearly with brood size or reached a threshold, we pooled provisioning by males and females and compared the fit of a linear versus quadratic regression model at each of the three stages with ANOVA F -tests (Zuur et al. 2010). We used LME models and model selection techniques (AIC c ) to investigate the factors that best predicted nestling mass at each of the three nestling stages. Brood size was the only main effect for Stages 1 ( n 1⁄4 58 nests) and 2 ( n 1⁄4 53 nests), but in Stage 3 ( n 1⁄4 40 nests) when we could sex nestlings by plumage, we also included sex and an interaction between sex and brood size. Nest of rearing was included as a random effect to account for multiple nestlings within each brood (fitted as a random intercept). Wing chord, as a dependent variable, was assessed at Stage 3 using an LME model with the same fixed and random effects used for nestling mass. Because not all nestlings were measured at exactly the same age and we wanted to pool nestlings in the LME models, we standardized body mass (at Stages 1 and 2) and wing length based on average values for a particular age from the growth curves of control nestlings in Gow et al. (2013a). We did not standardize mass at Stage 3 because mass plateaus during this stage (Gow et al. 2013a). Because of model selection uncertainty, we focused on parameter estimates ( 6 unconditional SE) and unconditional 95% CI for the fixed parameters in the selection of a ‘‘ best ’’ wing chord LME model. Logistic regression was used to determine the odds ratio of at least one nestling dying according to brood size. To test whether clutch sizes may be individually optimized, we compared the chance of nestling mortality and fledging success in manipulated broods to that of control broods of the same size within the natural population collected over 14 yr. Similarly, we compared fledging mass between manipulated and control broods of the same size within the natural population (collected over a span of 7 yr). The mass at fledging of these control nestlings did not differ between years (ANCOVA: F 9,1462 1⁄4 2.01, P 1⁄4 0.12) and neither did the fledging success ( F 9,819 1⁄4 1.85, P 1⁄4 0.11) so the years were similar in environmental conditions. All analyses were conducted in R version 2.15.2 (R Core Development Team 2012). LME models were run using the lme4 package (Bates et al. 2012) and AIC c values, weights, and natural model-averaging of parameter estimates were run using the AICcmodavg package (Mazerolle 2013). Unless otherwise indicated, data are reported as means 6 SE, with statistical significance set at a 0.05. Averaged over brood sizes, treatments, and years, the mean provisioning rate by males ( n 1⁄4 40) was 1.50 6 0.08, 1.72 6 0.06, and 1.39 6 0.17 trips per hr and for females ( n 40) was 1.41 6 0.07, 1.69 6 0.07, and 1.21 6 0.17 trips per hr for Stages 1–3, respectively. The maximum rate of 5–6 trips/hr occured in the largest broods of 10–11 nestlings at the oldest nestling stage (Figure 2). A post hoc test of the total number of provisioning visits to the nest (males and females pooled) varied according to nestling stage (ANOVA: F 2,142 1⁄4 4.41, P 1⁄4 0.01), with an increase between Stages 1 and 2 (Tukey HSD: P 1⁄4 0.03), but not between Stages 2 and 3 ( P 1⁄4 0.98). Feeding rates between partners did not differ at any stage of the nestling period (paired t -test: Stage 1: t 52 1⁄4 0.99, P 1⁄4 0.32; Stage 2: t 50 0.43, P 1⁄4 0.67; Stage 3: t 39 1⁄4 0.32, P 1⁄4 0.75). Except for males at Stage 2, brood size always appeared in the top model for provisioning rates and led to a summed w i of 0.98 (except for males at Stage 2: w i 0.26 and females Stage 1: w i 1⁄4 0.59; Table 1). Parameter estimates ( 6 unconditional SE) indicated that males and females increased provisioning with brood size at all stages (Supplemental Material Table S1A). Body condition also sometimes appeared in the top model with brood size, but the unconditional CI overlapped zero indicating non-significance. The best model for provisioning rate in relation to brood size was a linear increase at Stage 1, a decelerating quadratic curve at Stage 2, and an increasing curve at Stage 3 (Figure 2). Despite the increase in provisioning rates with brood size, per- nestling provisioning rates decreased with brood size (ANOVA: Stage 1: F 1,51 1⁄4 28.27, P , 0.001; Stage 2: F 1,49 71.21, P , 0.001; Stage 3: F 1,39 1⁄4 7.09, P , 0.01). The decline in per-nestling provisioning rates was fairly linear across the range of brood sizes except at Stage 3 (Figure 3). At Stages 1 and 2, nestlings in the smallest broods received about twice as many feedings per hour as those in the largest broods. Within each stage, nestling mass decreased with brood size (Table 2, Supplemental Material Table S1B, Figure 4). The interaction between brood size and nestling sex was the best model at Stage 3 (Table 2; w i 1⁄4 0.75); mass of male nestlings declined more dramatically with increasing brood size compared to that of females (Figure 4). The wing chord of male nestlings at Stage 3 (116 6 0.7 mm, n 126) did not differ significantly from females (116 6 0.6 mm, n 1⁄4 139; Table 2). Standardized to age, wing chord length decreased with increasing brood size (Table 2, Figure 5). According to parameter estimates and uncon- ditioned CI, only brood size and not sex explained variation in wing length at fledging (Supplemental Material Table S1B). Ten of 16 enlarged nests (63%) experienced nestling mortality as did 8 of 12 control nests (66%) and 2 of 13 reduced nests (15%). Of the 41 broods in the experiment that made it through Stage 3, a single nestling died in 10 cases, 2 nestlings died in 6 cases, and 3 nestlings died in 4 broods. The likelihood that at least one nestling died did not differ between control and enlarged nests ( v 21 1⁄4 0.22, P 0.64), however the likelihood of mortality tended to be lower in reduced compared to control nests ( v 21 1⁄4 3.6, P 0.06) and was significantly lower in reduced compared to enlarged broods ( v 21 1⁄4 5.33, P 1⁄4 0.02). The likelihood of mortality increased with brood size (logistic regression: odds ratio 1⁄4 1.58, 95% CI: 1.18–2.28, P 1⁄4 0.005). To see how mortality changed in relation to the degree of manipulation away from the original brood size, we classified nests according to the number of nestlings added or removed. The likelihood that at least one nestling died declined as nestlings were removed ( v 1⁄4 13.5, P 0.009), but did not increase in enlarged broods as more nestlings were added. Although the number of nestlings that died increased with brood size, the number of nestlings that fledged also increased with brood size (ANOVA: brood size effect: F 1,39 120.6, P , 0.001; treatment effect: F 2,38 1⁄4 33.23, P , 0.001; Figure 6). To determine whether clutch size may be individually optimized, we compared the productivity (number of fledglings) of manipulated broods to control broods of the same size from the natural population. There was no difference in fledging success between enlarged broods and the control broods from the natural population (2-way ANOVA: treatment group effect: F 1,491 1⁄4 0.33, P 0.56; brood size effect: F 3,491 1⁄4 12.89, P , 0.001, interaction effect: F 3,491 1⁄4 0.75, P 1⁄4 0.52; Figure 6). Similarly, the number of fledglings from experimentally reduced broods did not differ from the control broods in the natural population (2-way ANOVA: treatment group effect: F 1,510 1⁄4 0.60, P 1⁄4 0.44; brood size effect: F 3,510 252.38, P , 0.001, interaction effect: F 3,510 1⁄4 0.14, P 1⁄4 0.93; Figure 6). In the comparison of nestling mass between enlarged broods and control broods from the natural population, there was a significant interaction between brood size and treatment group (2-way ANOVA: treatment group effect: F 1,1138 1⁄4 0.53, P 1⁄4 0.47, brood size effect: F 4,1138 1⁄4 13.14, P , 0.001, interaction effect: F 4,1138 1⁄4 4.20, P , 0.01). Inspection of the data showed that for broods 8, nestlings were of lower mass in the enlarged broods compared to those in the control broods from the natural population. Nestlings in reduced broods were significantly heavier than nestlings in the natural population (2-way ANOVA: treatment group effect: F 1,768 1⁄4 14.41, P , 0.001; brood size effect: F 3,768 1⁄4 6.91, P , 0.001, interaction effect: F 1⁄4 0.29, P 1⁄4 0.84). Brood size manipulations in flickers revealed 2 main findings. First, both male and female parents followed a flexible rather than fixed provisioning strategy in relation to brood demands, and second, parents could rear more offspring than their original brood size. Provisioning rates and brood size were positively correlated in our control broods and in unmanipulated flicker broods in the population (this study; Gow et al. 2013a) but our current experiment confirms that parents can assess demands from the brood and adjust their effort ‘‘ in real time ’’ as brood size both decreases and increases. Gow (2014) reviewed brood size experiments and found that only 50% of 6 studies on long-lived birds ( . 80% adult survival rate) with a ‘‘ slow ’’ life history (mainly seabirds and raptors) ...
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... In contrast, longlived species will favour their own survival and/or their future reproduction (Ghalambor & Martin, 2001;Hamel et al., 2010). Brood size (Koenig & Walters, 2012;Musgrove & Wiebe, 2014) and food resource manipulations (Markman et al., 2002) can modify the reproductive costs of the parents. Both experimental approaches allow us to measure the ability or willingness of the parents to invest in current or future reproduction. ...
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en The behavior of young songbirds after fledging is one of the least understood phases of the breeding cycle, although parental provisioning rates and movement of fledglings are key to understanding life history evolution. We studied Cordilleran Flycatchers (Empidonax occidentalis) at two sites in southwestern Colorado, USA, from 2012 to 2017. We banded and sexed breeding adults to determine the relative contributions of males and females to nestling and fledgling care, and attached radio‐transmitters to nestlings to facilitate observations of brood behavior after fledging. Females made 60% and 78% of total observed feedings of nestlings and fledglings, respectively. Parental provisioning rates increased with nestling age, and per‐nestling provisioning rates increased with brood size. Parental provisioning rates declined just before fledging, then increased just after fledging. Fledging times of individuals in broods were asynchronous and concentrated during the late afternoon and early evening. Males stopped caring for fledglings before females even though this species is single‐brooded, with some late‐season broods being abandoned by males. Broods spent the first three weeks after fledging within 400 m of nests, after which they began to disperse. Most aspects of the breeding biology of Cordilleran Flycatchers in our study, including the duration of nestling and fledging periods, female‐dominated provisioning, and movement patterns of fledglings, were similar to those of other Empidonax species. However, the times when young fledged were not concentrated in the morning as reported in most other songbirds, and this result warrants additional study of the timing of fledging in ecologically and taxonomically similar species. The increased per‐nestling provisioning rate with increasing brood size was unexpected, and additional study is needed to determine if this increase results from a trade‐off between adult annual survival and productivity favoring increased provisioning of young in larger broods, or from the existence of high‐quality individuals where larger clutches and higher provisioning rates are linked.
RESUMEN
es Tasas de provisión de cría y comportamiento de volantones de los papamoscas amarillos barranqueños en el suroeste de Colorado
El comportamiento de los pájaros cantores jóvenes después de abandonar el nido es una de las fases menos comprendidas del ciclo de reproducción, aunque las tasas de provisión de los padres y el movimiento de los volantones son clave para comprender la evolución del ciclo de vida. Estudiamos los papamoscas amarillos barranqueños (Empidonax occidentalis) en dos sitios en el suroeste de Colorado, EE. UU., De 2012 a 2017. Anillamos y sexamos a los adultos reproductores para determinar las contribuciones relativas de machos y hembras al cuidado de los pichones y los volantones, y colocamos transmisores de radio a los pichones para facilitar las observaciones del comportamiento de las crías después de abandonar el nido. Las hembras representaron el 60% y el 78% de la alimentación total observada de pichones y volantones, respectivamente. Las tasas de aprovisionamiento de los padres aumentaron con la edad de los pichones y las tasas de aprovisionamiento de los pichones aumentaron con el tamaño de la cría. Las tasas de aprovisionamiento parental disminuyeron justo antes del abandono el nido, luego aumentaron justo después del abandono. Los tiempos de abandono del nido de los individuos en las nidadas fueron asincrónicos y concentrados durante las últimas horas de la tarde y las primeras horas de la noche. Los machos dejaron de cuidar a los volantones antes que las hembras, aunque esta especie es de un sólo evento de nidada, y algunos machos abandonan algunas nidadas tardías. Las crías pasaron las primeras tres semanas después de dejar el nido dentro de los 400 m de los nidos, después de lo cual comenzaron a dispersarse. La mayoría de los aspectos de la biología reproductiva de los papamoscas amarillos baranqueños en nuestro estudio, incluida la duración de los períodos de cría y de volatón, el aprovisionamiento dominado por las hembras y los patrones de movimiento de los volantones, fueron similares a los de otras especies de Empidonax. Sin embargo, los momentos en que los volantones dejan el nido no se concentraron en la mañana como se informó en la mayoría de los otros pájaros cantores, y este resultado justifica un estudio adicional del momento de abandono de nido en especies ecológica y taxonómicamente similares. El aumento de la tasa de aprovisionamiento por nido con el aumento del tamaño de la nidada fue inesperado, y se necesitan estudios adicionales para determinar si este aumento es el resultado de una compensación entre la supervivencia anual del adulto y la productividad que favorece un mayor aprovisionamiento de crías en nidadas más grandes, o de la existencia de individuos de alta calidad donde se interconectan tamaños mayores de cría y tasas de aprovisionamiento más altas.
... In addition to prey type and size, higher delivery rates to nestlings also tend to increase their growth. Experimental enlargements of brood size have shown that parents of many species of birds increase delivery rate to nestlings, presumably in response to higher brood demands and begging cues from nestlings (Musgrove and Wiebe 2014) to try to maintain the quality of offspring. However, if the demands of the brood are too high, increased delivery rates can be associated with a decrease in prey quality and hence reduced nutrition to nestlings, if parents are overly stressed and spend less time searching out the high-quality prey (Wright et al. 1998, García-Navas and Sanz 2010, Wiebe and Slagsvold 2015. ...
Clear-cutting of forests results in early successional stages that resemble grasslands, and grassland birds such as Mountain Bluebirds (Sialia currucoides) may settle in these anthropogenically created habitats to breed. Our objective was to determine if parent bluebirds provisioned offspring differently, in terms of amount and quality of prey, in clear-cuts versus grasslands, and how this related to fledgling production. We placed microcameras inside 92 nestboxes during two breeding seasons to film parental food deliveries at sites in central British Columbia. At the young nestling stage (< 5 d old), there were no significant differences in terms of provisioning rate or the type of prey delivered. Neither the abundance of perches in the habitat nor parental traits such as age or plumage brightness were associated with provisioning. When nestlings were older, parents in clear-cuts delivered slightly larger prey and diets with proportionately more larvae and spiders, the most nutritious taxa. However, delivery rates were 21% higher in grasslands than in clear-cuts. Fledglings in clear-cuts had lighter body mass than those in grasslands, suggesting that the high nutrient content of prey in clear-cuts could not compensate for the lower deliveries. Thus, parents in grasslands seem more able to meet the energy demands of large nestlings by incorporating diverse insect taxa into their diet.
... The age of neither parent affected the proportion of the brood that survived but there was a weak positive effect of having a familiar partner. Musgrove and Wiebe (2014) found that both sexes responded to experimentally enlarged broods by increasing delivery rates to nestlings. Hence, female flickers that strategically lay large clutches for their mates can expect him to increase provisioning to match the brood size and, because males provision about 10% more than females (Gow et al. 2013b), he will also invest proportionately more than the female in feeding "extra" offspring. ...
Age-related improvement in reproductive performance is widespread in vertebrates and constraints at young ages are a common cause. The sex that invests energetically more in reproduction, typically the female, is predicted to show stronger age-related performance but the effect of the male’s age on reproduction has often been ignored. I studied age-related reproduction of both sexes in northern flickers, in which males invest more parental care than females, predicting that the effect of age would be stronger in males than in females. Longitudinal data on individuals collected during an 18-year field study confirmed this prediction. Laying dates for females improved only between the first 2 years of her life and no other reproductive parameter changed over her lifetime when the male’s age was statistically controlled. In contrast, males improved up to age five for laying date, clutch size, hatching success and fledging success. Partner familiarity (fidelity) was further associated with earlier laying, larger clutches, improved fledging success and more fledglings. There was significant assortative pairing by age but there is apparently little benefit for males to choose older females, but a benefit to females with older males. Females appear to strategically lay larger clutches when paired to old males which invest more in paternal care than younger males. This is the first example of clutch size being influenced by only male age and not female age in any bird and suggests that sex roles in parental care are important determinants of aging patterns in vertebrates with diverse life histories.
... Under mild fluctuations in environmental conditions, like a minor reduction in food availability, parents may compensate through behavioural flexibility [29][30][31]. Functionally, the elevation of baseline CORT in response to environmental challenges can mobilize fat reserves for energetically demanding behaviours [32]. In birds, elevated baseline CORT has been associated with increased foraging duration [33] and nestling provisioning rates [34,35]. ...
Glucocorticoids, including corticosterone (CORT), have been suggested to provide a physiological link between ecological conditions and fitness. Specifically, CORT, which is elevated in response to harsh conditions, is predicted to be correlated with reduced fitness. Yet, empirical studies show that CORT can be non-significantly, positively and negatively linked with fitness. Divergent environmental conditions between years or study systems may influence whether CORT is linked to fitness. To test this, we monitored free-living blue tits (Cyanistes caeruleus) during breeding over 3 years. We quantified foraging conditions during brood rearing, and examined whether they were correlated with parental baseline CORT and reproductive success.We then tested whether CORT predicted fitness. Elevated parental CORT was associated with lower temperatures, greater rainfall and lower territory-scale oak density. Whereas asynchrony with the caterpillar food peak was correlated with reduced nestling mass and fledging success, but not parental CORT. Only low temperatures were associated with both reduced nestling mass and elevated parental CORT. Despite this, parents with elevated CORT had lighter offspring in all years. Contrarily, in 2009 parental CORT was positively correlated with the number fledged. The absence of a direct link between the foraging conditions that reduce nestling quality and elevate parental CORT suggests that parental CORT may provide a holistic measure of conditions where parents are working harder to meet the demands of developing young. As the positive correlation between parental CORT and fledging success differed between years, this suggests that contrasting conditions between years can influence correlations between parental CORT and fitness. Ultimately, as CORT concentrations are intrinsically variable and linked to the prevalent conditions, studies that incorporate environmental harshness will improve our understanding of evolutionary endocrinology.
... For example, relative to nestlings from control broods, nestlings from experimentally enlarged broods in which food was scarce had lower T-cell-mediated immunocompetence (Saino et al. 1997, Hõrak et al. 1999, Ilmonen et al. 2003, increased baseline CORT levels (Saino et al. 2003), decreased deposition of carotenoids (Hõrak et al. 2000) and smaller melanin patches (Piault et al. 2012), presumably because these traits are resource-demanding. We previously showed (Musgrove & Wiebe 2014) that experimental manipulation of brood size in Northern Flickers Colaptes auratus caused reduced per-nestling provisioning by parents and lower mass of nestlings in enlarged broods, compared with reduced broods. Furthermore, we found that the size of eumelanin breast spots was larger in nestlings from reduced, compared with enlarged broods (Wiebe & Vitousek 2015), and that carotenoid colour in nestling wing feathers increased with body mass (Musgrove & Wiebe 2015). ...
... Both parents contribute to nestling provisioning, but males provision more (Gow et al. 2013). There is no evidence of extra-pair paternity in the species (Wiebe & Kempenaers 2009) and parents readily accept foster nestlings (Musgrove & Wiebe 2014). ...
... During 2012 and 2013, we manipulated brood sizes 2-4 days after hatching by transferring one to four nestlings from a reduced to an enlarged brood, thereby increasing brood size by approximately 40% (see Musgrove & Wiebe 2014 for details). Manipulated and control broods were matched with respect to hatching date (AE 1 day) and brood sizes were maintained within the natural range for the species. ...
Visual signals of quality in offspring, such as plumage colour, should honestly advertise need and/or body condition, but links between nutritional status, physiological performance and the expression of colours are complex and poorly understood. We assess how food stress during rearing affected two physiological measures (T-cell mediated immune function and corticosterone level in feathers: CORTf) and how these two variables were related to carotenoid and melanin colouration in Northern Flicker Colaptes auratus nestlings. We were also interested in how these two physiological measures were influenced by the sex of the nestling. We experimentally manipulated brood size to alter levels of food availability to nestlings during development. We measured carotenoid-based colour (chroma and brightness) in wing feathers and the size of melanin spots on breast feathers. In agreement with our prediction, nestlings in the reduced brood treatment had better body condition and stronger immune responses than those in the control and brood enlargement treatments. This supports the hypothesis that immune responses are energetically costly. In contrast, CORTf was not related to nestling body condition or sex and was unaffected by brood size manipulation. Nestlings of both sexes with stronger T-cell mediated immune responses had larger melanin spots but only males with higher immune responses also had brighter flight feathers. Feather brightness decreased with increasing CORTf levels. Our study is one of few to examine the relationship between multiple physiological and plumage measures in nestlings and shows that plumage colour and immune function signalled body condition of nestlings, but that feather corticosterone levels did not. This article is protected by copyright. All rights reserved.
... Other favoured/unfavoured states include competing with few/ many siblings, being early/late in the hatching order, and potentially sex. Socially, minimizing competition with siblings is often advantageous, resulting in better condition (Alonso-Alvarez et al., 2006), higher fledging size, and reduced mortality (Musgrove & Wiebe, 2014). It is usually better to be the firstborn or first hatched (Coulson & Porter, 1985;White et al., 2010), as these offspring are typically dominant and more competitive (Drummond & Chavelas, 1989). ...
Early-life conditions can drive ageing patterns and life history strategies throughout the lifespan. Certain social, genetic, and nutritional developmental conditions are more likely to produce high-quality offspring: those with good likelihood of recruitment and productivity. Here we call such conditions "favored states" and explore their relationship with physiological variables during development in a long-lived seabird, the black-legged kittiwake (Rissa tridactyla). Two favored states were experimentally generated by manipulation of food availability and brood size, while hatching order and sex were also explored as naturally generating favored states. Thus, the favored states we explored were high food availability, lower levels of sibling competition, hatching first, and male sex. We tested the effects of favored developmental conditions on growth, stress, telomere length (a molecular marker associated with lifespan), and nestling survival. Generation of favored states through manipulation of both the nutritional and social environments furthered our understanding of their relative contributions to development and phenotype: increased food availability led to larger body size, reduced stress, and higher antioxidant status, while lower sibling competition (social environment) led to lower telomere loss and longer telomere lengths in fledglings. Telomere length predicted nestling survival, and wing growth was also positively correlated with telomere length, supporting the idea that telomeres may indicate individual quality, mediated by favored states. This article is protected by copyright. All rights reserved.
... This effect is more pronounced during food scarcity [16,19,21]. Simultaneously, nestlings from enlarged broods usually reach a lower body mass and shorter wings compared to those from smaller broods, due to increased sibling competition [22][23][24][25]. Moreover, the larger sex usually grows faster than the smaller sex and reaches a higher body mass [26][27][28][29]. ...
1. Camera recording and video analysis have emerged as a successful non-invasive method for collecting a wide range of biological data on many different taxa of animals. However, camera monitoring has rarely been applied to long term surveillance of cavity or box-nesting species and ordinary off-the-shelf cameras are employed.
2. We present methodology and data on the effectiveness of nest box monitoring using a camera system embedded in four “smart nest boxes” (SNBoxes). We applied the SNBoxes to eight Tengmalm’s owl (Aegolius funereus) nests in the Czech Republic during a five-month period in 2014. Each SNBox consisted of a pair of cameras with infrared lighting, an event detector, a radio-frequency identification reader, auxiliary sensors, and a 60 Ah 12 V battery to power the whole system. All devices used were centrally managed by an embedded computer with specifically developed software.
3. Using four SNBoxes, we observed owl nesting continually during the incubation, nestling, and fledgling phases, in total 309 days, resulting in 3382 owl video events. Batteries were changed every 6.5 days. A memory of 4 GB was found sufficient to store monthly data. We identified 12 types of male and female parental activities and their timing, the diet composition and frequency of prey delivery, the manner of prey storage, the light intensity at the time of each parental activity, the temperature inside the clutch and outside the box, and the duration of nestling period of each young. We also produced a video on owl nesting for the general public.
4. The SNBox and related methodology show enormous potential as a non-invasive tool for monitoring animals using boxes or natural cavities. The main advantage of the SNBox is the possibility to study both nocturnal and diurnal animal species and great flexibility in use of the software and hardware for different tasks. As a result, the SNBox provides an opportunity for novel insights into the breeding, roosting, hibernating, and food storage activities of a wide range of cavity-living birds, mammals, and reptiles.
... This effect is more pronounced during food scarcity [16,19,21]. Simultaneously, nestlings from enlarged broods usually reach a lower body mass and shorter wings compared to those from smaller broods, due to increased sibling competition [22][23][24][25]. Moreover, the larger sex usually grows faster than the smaller sex and reaches a higher body mass [26][27][28][29]. ...
In altricial birds, energy supply during growth is a major predictor of the physical condition and survival prospects of fledglings. A number of experimental studies have shown that nestling body mass and wing length can vary with particular extrinsic factors, but betweenyear observational data on this topic are scarce. Based on a seven-year observational study in a central European Tengmalm’s owl population we examine the effect of year, brood size, hatching order, and sex on nestling body mass and wing length, as well as the
effect of prey abundance on parameters of growth curve. We found that nestling body mass varied among years, and parameters of growth curve, i.e. growth rate and inflection point in particular, increased with increasing abundance of the owl’s main prey (Apodemus mice, Microtus voles), and pooled prey abundance (Apodemus mice, Microtus voles, and Sorex shrews). Furthermore, nestling body mass varied with hatching order and between sexes being larger for females and for the first-hatched brood mates. Brood size had no effect on nestling body mass. Simultaneously, we found no effect of year, brood size, hatching order,
or sex on the wing length of nestlings. Our findings suggest that in this temperate owl population, nestling body mass is more sensitive to prey abundance than is wing length. The latter is probably more limited by the physiology of the species.
