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# Elevated nest predation risk promotes offspring size variation in birds with prolonged parental care.

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## Abstract

Predation of offspring is the main cause of reproductive failure in many species, and the mere fear of offspring predation shapes reproductive strategies. Yet, natural predation risk is ubiquitously variable and can be unpredictable. Consequently, the perceived prospect of predation early in a reproductive cycle may not reflect the actual risk to ensuing offspring. An increased variance in investment across offspring has been linked to breeding in unpredictable environments in several taxa, but has so far been overlooked as a maternal response to temporal variation in predation risk. Here, we experimentally increased the perceived risk of nest predation prior to egg-laying in seven bird species. Species with prolonged parent-offspring associations increased their intra-brood variation in egg, and subsequently offspring, size. High risk to offspring early in a reproductive cycle can favour a risk-spreading strategy particularly in species with the greatest opportunity to even out offspring quality after fledging.

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... This resource constraint may not be experienced equally by all nestlings. As a form of brood reduction, adults may provision larger nestlings preferentially in order to fledge a partial brood of the largest nestlings quickly rather than losing the entire brood (Wagner et al. 2019). Additionally, nestlings may be able to detect predator presence and respond adaptively, such as by reducing begging upon hearing a predator (direct effect ;Magrath et al. 2010). ...
Thesis
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Offspring development is a critical life-history stage for altricial songbirds and a prime target for selection, as predation risk is high relative to other life-stages and environmental conditions can induce lasting consequences for life-time fitness. Nestling development rates vary widely among species, populations, and individuals. Rapid development is considered an evolved response to improve nest success given high predation risk at the species or population level. However, it is unclear what drives variation in development rate among individuals and whether offspring or parents have the adaptive capacity to respond to prevailing stressors. I investigated the relative influence of multiple, interacting drivers from across the annual cycle on offspring developmental variation within an alpine breeding population of horned lark (Eremophila alpestris) by integrating ecological observations, behavioural experiments, physiology, and light-level geolocators to track migration. I demonstrated that rapid development was associated with a greater probability of nest success, confirming a selective advantage to fledging quickly. Cold ambient temperatures during the nestling period prolonged development, potentially due to resource constraints or thermoregulatory challenges, but females in better body condition were able to buffer offspring against harsh, early season conditions, enabling rapid development. With elevated predation risk, nestlings left the nest earlier by increasing wing growth. This effect was mediated by predator-specific glucocorticoid responses (stress biomarker) and parental provisioning behaviour. During spring migration, I showed that 59% of adults conducted extended stopovers (mean = 41 days) and subsequently had greater reproductive success during the breeding season. However, periods of extreme cold during stopover were correlated with prolonged offspring development, resulting in a lower probability of nest success. My results demonstrate that: 1) nestlings have the adaptive ability to respond to elevated predation risk, 2) parental care can mediate offspring development in response to suboptimal conditions, and 3) prolonged stopovers may be key components of the annual cycle for alpine larks. By addressing within-population variation, I offer new insights into the eco-evolutionary drivers that shape offspring development across the annual cycle with implications for individual fitness and, ultimately, population-level responses to rapidly changing environments.
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Predation on dependent offspring (i.e., offspring that depend on parents for care) forms a critical source of natural selection that may shape a diversity of life history traits. Selection from predation risk on dependent offspring can influence life history strategies of both offspring and parents. Such selection may act on both the form of plastic responses (e.g., the shape of norms of reaction) and mean expression of traits. Consideration of both levels of responses is key to understanding the ecological and evolutionary role of predation on dependent offspring. Here, we discuss how plastic responses and mean expression of life history traits may respond to selection from predation on dependent offspring in nests of birds (i.e., nest predation). We then review the expected effects and evidence for a diversity of life history traits, including clutch size, egg size, renesting rates, onset of incubation, parental incubation behavior, development rates and period lengths, parental feeding behavior, nestling begging, and nest conspicuousness. The evidence demonstrates a broad role of nest predation on both phenotypic plasticity and mean expression of diverse traits, but evidence remains limited to a few studies on a limited variety of species for almost all traits, and much broader experimental tests are needed.
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Predation is a strong selective force acting on prey animals. Predation is by nature highly variable in time; however, this aspect of predation risk has traditionally been overlooked by behavioural ecologists. Lima and Bednekoff proposed the predation risk allocation hypothesis (RAH), predicting how temporal variation in predation risk drives prey antipredator behaviours. This model is based on the concept that prey adaptively allocate their foraging and antipredator efforts across high- and low-risk situations, depending on the duration of high- vs. low-risk situations and the relative risk associated with each of them. An unstudied extension of the RAH is the effect of predictability of predation risk. A predictable risk should lead to prey displaying minimal vigilance behaviours during predictable low-risk periods and the strongest antipredator behaviours during risky periods. Conversely, an unpredictable predation risk should result in prey displaying constant vigilance behaviour, with suboptimal foraging rates during periods of safety but antipredator behaviours of lower intensity during periods of risk. We tested this extension of the RAH using convict cichlids exposed to high-risk alarm cues at two frequencies of risk (1× vs. 3×) per day, on either a fixed or random schedule for 5 d. We then tested the fish for a response to high-risk cues (alarm cues) and to low-risk cues (disturbance resulting from the introduction of distilled water). Our study supports previous results on the effects of risk frequency and cue intensity on cichlid behaviour. We failed to show an effect of risk predictability on the behavioural responses of cichlids to high-risk alarm cues, but predictability did influence responses to low-risk cues. We encourage further studies to test the effect of predictability in other systems.
Article
Predators can affect individual fitness and population and community processes through lethal effects (direct consumption or ‘density’ effects), where prey is consumed, or through non-lethal effects (trait-mediated effects or interactions), where behavioural compensation to predation risk occurs, such as animals avoiding areas of high predation risk. Studies of invertebrates, fish and amphibians have shown that non-lethal effects may be larger than lethal effects in determining the behaviour, condition, density and distribution of animals over a range of trophic levels. Although non-lethal effects have been well described in the behavioural ecology of birds (and also mammals) within the context of anti-predation behaviour, their role relative to lethal effects is probably underestimated. Birds show many behavioural and physiological changes to reduce direct mortality from predation and these are likely to have negative effects on other aspects of their fitness and population dynamics, as well as affecting the ecology of their own prey and their predators. As a consequence, the effects of predation in birds are best measured by trade-offs between maximizing instantaneous survival in the presence of predators and acquiring or maintaining resources for long-term survival or reproduction. Because avoiding predation imposes foraging costs, and foraging behaviour is relatively easy to measure in birds, the foraging–predation risk trade-off is probably an effective framework for understanding the importance of non-lethal effects, and so the population and community effects of predation risk in birds and other animals. Using a trade-off approach allows us to predict better how changes in predator density will impact on population and community dynamics, and how animals perceive and respond to predation risk, when non-lethal effects decouple the relationship between predator density and direct mortality rate. The trade-off approach also allows us to identify where predation risk is structuring communities because of avoidance of predators, even when this results in no observable direct mortality rate.
Article
Evolutionary bet-hedging involves a trade-off between the mean and variance of fitness, such that phenotypes with reduced mean fitness may be at a selective advantage under certain conditions. The theory of bet-hedging was first formulated in the 1970s, and recent empirical studies suggest that the process may operate in a wide range of plant and animal species.
Article
Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form. Markov chain Monte Carlo methods solve this problem by sampling from a series of simpler conditional distributions that can be evaluated. The R package MCMCglmm implements such an algorithm for a range of model fitting problems. More than one response variable can be analyzed simultaneously, and these variables are allowed to follow Gaussian, Poisson, multi(bi)nominal, exponential, zero-inflated and censored distributions. A range of variance structures are permitted for the random effects, including interactions with categorical or continuous variables (i.e., random regression), and more complicated variance structures that arise through shared ancestry, either through a pedigree or through a phylogeny. Missing values are permitted in the response variable(s) and data can be known up to some level of measurement error as in meta-analysis. All simu- lation is done in C/ C++ using the CSparse library for sparse linear systems.
Article
[1st paragraph] At first sight, Bayesian inference with Markov Chain Monte Carlo (MCMC) appears to be straightforward. The user defines a full probability model, perhaps using one of the programs discussed in this issue; an underlying sampling engine takes the model definition and returns a sequence of dependent samples from the posterior distribution of the model parameters, given the supplied data. The user can derive any summary of the posterior distribution from this sample. For example, to calculate a 95% credible interval for a parameter α, it suffices to take 1000 MCMC iterations of α and sort them so that α1<α2<...<α1000. The credible interval estimate is then (α25, α975). However, there is a price to be paid for this simplicity. Unlike most numerical methods used in statistical inference, MCMC does not give a clear indication of whether it has converged. The underlying Markov chain theory only guarantees that the distribution of the output will converge to the posterior in the limit as the number of iterations increases to infinity. The user is generally ignorant about how quickly convergence occurs, and therefore has to fall back on post hoc testing of the sampled output. By convention, the sample is divided into two parts: a “burn in” period during which all samples are discarded, and the remainder of the run in which the chain is considered to have converged sufficiently close to the limiting distribution to be used. Two questions then arise: 1. How long should the burn in period be? 2. How many samples are required to accurately estimate posterior quantities of interest? The coda package for R contains a set of functions designed to help the user answer these questions. Some of these convergence diagnostics are simple graphical ways of summarizing the data. Others are formal statistical tests.
Article
Offspring size is strikingly variable within species. Although theory can account for variation in offspring size among mothers, an adaptive explanation for variation within individual broods has proved elusive. Theoretical considerations of this problem assume that producing offspring that are too small results in reduced offspring viability, but producing offspring that are too large (for that environment) results only in a lost opportunity for increased fecundity. However, logic and recent evidence suggest that offspring above a certain size will also have lower fitness, such that mothers face fitness penalties on either side of an optimum. Although theory assuming intermediate optima has been developed for other diversification traits, the implications of this idea for selection on intra-brood variance in offspring size have not been explored theoretically. Here we model the fitness of mothers producing offspring of uniform vs. variable size in unpredictably variable environments and compare these two strategies under a variety of conditions. Our model predicts that producing variably sized offspring results in higher mean maternal fitness and less variation in fitness among generations when there is a maximum and minimum viable offspring size, and when many mothers under- or overestimate this optimum. This effect is especially strong when the viable offspring size range is narrow relative to the range of environmental variation. To determine whether this prediction is consistent with empirical evidence, we compared within- and among-mother variation in offspring size for five phyla of marine invertebrates with different developmental modes corresponding to contrasting levels of environmental predictability. Our comparative analysis reveals that, in the developmental mode in which mothers are unlikely to anticipate the relationship between offspring size and performance, size variation within mothers exceeds variation among mothers, but the converse is true when optimal offspring size is likely to be more predictable. Together, our results support the hypothesis that variation in offspring size within broods can reflect an adaptive strategy for dealing with unpredictably variable environments. We suggest that, when there is a minimum and a maximum viable offspring size and the environment is unpredictable, selection will act on both the mean and variance of offspring size.
Article
Phenotypic plasticity itself evolves, as does any other quantitative trait. A very different question is whether phenotypic plasticity causes evolution or is a major evolutionary mechanism. Existing models of the evolution of phenotypic plasticity cover many of the proposals in the literature about the role of phenotypic plasticity in evolution. I will extend existing models to cover adaptation to a novel environment, the appearance of ecotypes and possible covariation between phenotypic plasticity and mean trait value of ecotypes. Genetic assimilation does not sufficiently explain details of observed patterns. Phenotypic plasticity as a major mechanism for evolution--such as, invading new niches, speciation or macroevolution--has, at present, neither empirical nor model support.
Article
Risk effects arise when prey alter their behavior in response to predators, and these responses carry costs. Empirical studies have found that risk effects can be large. Nonetheless, studies of predation in vertebrate conservation and management usually consider only direct predation. Given the ubiquity and strength of behavioral responses to predators by vertebrate prey, it is not safe to assume that risk effects on dynamics can be ignored. Risk effects can be larger than direct effects. Risk effects can exist even when the direct rate of predation is zero. Risk effects and direct effects do not necessarily change in parallel. When risk effects reduce reproduction rather than survival, they are easily mistaken for limitation by food supply.
Coping with environmental uncertainty: dynamic bet hedging as a maternal effect
• A J Crean
• D J Marshall
Crean, A. J. & Marshall, D. J. Coping with environmental uncertainty: dynamic bet hedging as a maternal effect. Philosophical Transactions of the Royal Society of London B: Biological Sciences 364, 1087-1096 (2009).