Body mass change strategies in blackbirds <i>Turdus merula</i>: the starvation–predation risk trade‐off

MacLeod, Ross; Barnett, Phil; Clark, Jacquie A.; Cresswell, Will

doi:10.1111/j.1365-2656.2005.00923.x

Cited by 103 publications

(101 citation statements)

References 42 publications

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“…The loss of BW during the night was explained by lower air temperature whereas a gradual weight increase during the day was due to active feeding. Although the study on daily variation in BW of wild birds continued (see for example the review by Clark (1979) or Macleod et al (2005)), not much attention has been given to daily variation in BW of farm animals. Daily measurements of farm animals are obviously far easier to obtain than those of wild living species.…”

Section: Discussionmentioning

confidence: 99%

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

Stygar

Dolecheck

Kristensen

2018

Animal

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Frequent BW monitoring of growing pigs can be useful for identifying production (e.g. feeding), health and welfare problems. However, in order to construct a tool which will properly recognize abnormalities in pigs' growth a precise description of the growth process should be used. In this study we proposed a new model of pig growth accounting for daily fluctuations in BW. Body weight measurements of 1710 pigs (865 gilts and 843 barrows) originating from five consecutive batches from a Danish commercial farm were collected. Pigs were inserted into a large pen (maximum capacity = 400) between November 2014 and September 2015. On average, each pig was observed for 42 days and weighed 3.6 times a day when passing from the resting to feeding area. Altogether, 243,160 BW measurements were recorded. A multilevel model of pig growth was constructed and fitted to available data. The BW of pigs was modeled as a quadratic function of time. A diurnal pattern was incorporated into the model by a cosine wave with known length (24 h). The model included pig effect which was defined as a random autoregressive process with exponential correlation. Variance of within-pigs error was assumed to increase with time. Because only five batches were observed, it was not possible to obtain the random effect for batch. However, in order to account for the batch effect the model included interactions between batch and fixed parameters: intercept, time, square value of time and cosine wave. The gender effect was not significant and was removed from the final model. For all batches, morning and afternoon peaks in the frequency of visits to the feeding area could be distinguished. According to results, pigs were lighter in the morning and heavier in the evening (minimum BW was reached around 1000 h and maximum around 2200 h). However, the exact time of obtaining maximum and minimum BW during the day differed between batches. Pigs had access to natural light and, therefore, existing differences could be explained by varying daylight level during observations periods. Because the diurnal amplitude for pig growth varied between batches from 0.9 to 1.4 kg, BW monitoring tools based on frequent measurements should account for diurnal variation in BW of pigs. This proposed description of growth will be built into a monitoring tool (a dynamic linear model) and applied to farm data in future studies.

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Section: Discussionmentioning

confidence: 99%

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

Stygar

Dolecheck

Kristensen

2018

Animal

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“…Mass data were normally distributed. We controlled for the effects of month (four-level factor, November-February), body size (wing length and sex), dominance (age), day length and time of day by including these variables in each model (Cresswell 1998;MacLeod et al 2005a). We controlled for spatial autocorrelation by including longitude and latitude of capture in all models.…”

Section: Methodsmentioning

confidence: 99%

“…For each first capture of an individual, age (first winter or second winter and older), sex, ring number, date, time and location of capture were recorded and standard measurements of wing length to 1 mm and mass to 0.1 g were made (Redfern & Clark 2001). Maximum air temperature, minimum air temperature, mean air temperature, day length and time since sunrise for each capture were obtained or calculated from data provided by the NERC British Atmospheric Data Centre or via the website of the Astronomical Applications Department of the US Naval Observatory (for details, see MacLeod et al 2005a). The dataset of mass measurements from 26 219 individual birds (each sampled only once) allowed us to identify whether mass was changing in response to hawk abundance (see below), and whether this change depended on year, controlling for important confounding variables that also affect starvation risk and therefore mass.…”

Section: Methodsmentioning

confidence: 99%

How climate change might influence the starvation–predation risk trade-off response

2009

Self Cite

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Climate change within the UK will affect winter starvation risk because higher temperatures reduce energy budgets and are likely to increase the quality of the foraging environment. Mass regulation in birds is a consequence of the starvation -predation risk trade-off: decreasing starvation risk because of climate change should decrease mass, but this will be countered by the effects of predation risk, because high predation risk has a negative effect on mass when foraging conditions are poor and a positive effect on mass when foraging conditions are good. We tested whether mass regulation in great tits (Parus major) across the UK was related to temporal changes in starvation risk (winter temperature 1995 -2005) and spatial changes in predation risk (sparrowhawk Accipiter nisus abundance). As predicted, great tits carried less mass during later, warmer, winters, demonstrating that starvation risk overall has decreased. Also, the effects of predation risk interacted with the effects of temperature (as an index of foraging conditions), so that in colder winters higher sparrowhawk abundance led to lower mass, whereas in warmer, later, winters higher sparrowhawk abundance led to higher mass. Mass regulation in a small bird species may therefore provide an index of how environmental change is affecting the foraging environment.

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“…When starvation risk is high during the long, cold nights and short days of winter most small birds put on fat reserves (e.g. MacLeod et al 2005;Macleod et al 2008) and adopt foraging areas and tactics that maximise their daily energy intake (e.g. van der Veen 2000; Duriez et al 2005).…”

Section: High Costs Of Compensating For Predation Riskmentioning

confidence: 99%

Predation in bird populations

Cresswell

2010

J Ornithol

222

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One of the classic ecological questions is how predators affect population size. This is often assessed by measuring how many individuals are killed by a predator, yet such direct effects may only be a relatively minor part of population dynamics. Predators frequently affect prey populations indirectly, with the fear of predation resulting in costly behavioural compensation leading to potentially large population and community effects. Large observable lethal effects may then just represent the most easily observed "special" cases of the effects of predation on populations, with the costs of non-lethal effects being ubiquitous and usually dominant. This review explores these two ideas: that cases where there are no population effects due to predation, and cases where lethal effects dominate, are unusual and involve special circumstances. First, systems where predation effects appear not to arise include complete avoidance of predators by prey; when other environmental factors limit populations so that predation is not additive to mortality; when there are other more vulnerable prey for a predator; when predators interact; because the relationship of perceived predation risk with predator abundance is usually a non-linear function, and simply because non-lethal effects have not been considered. Second, lethal effects tend to dominate over non-lethal effects when there is a high cost of compensating for predation risk associated with either resource constraint or a particularly vulnerable niche or life history stage (e.g. the nest stage generally for birds); when prey are the most popular prey of a predator or linear trophic chains operate; when there is evolutionary lag such as introduced predators and naïve prey populations; and when there are several predator species hunting the same prey in diverse ways. The presence of predators may or may not affect the size of a bird population at any particular life history stage, although in most cases it will through non-lethal effects and occasionally through lethal effects.But the presence of predators will always affect intra-and interspecific competition and so will always affect population dynamics. Studies that wish to fully demonstrate that predation has no effect on bird populations must show that lethal effects and the costs of non-lethal compensation by the prey do not significantly change its density and so the level of competition.3

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Body mass change strategies in blackbirds Turdus merula: the starvation–predation risk trade‐off

Cited by 103 publications

References 42 publications

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

How climate change might influence the starvation–predation risk trade-off response

Predation in bird populations

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