Sex-specific effects of the local social environment on juvenile post-fledging dispersal in great tits

An individual’s decision to disperse from the natal habitat can affect its future fitness prospects. Especially in species with sex-biased dispersal, we expect the cost–benefit balance for dispersal to vary according to the social environment (e.g., local sex ratio and density). However, little is known about the social factors affecting dispersal decisions and about the temporal and spatial patterns of the dispersal process. In our study, we investigated experimentally the effects of the social environment on post-fledging dispersal of juvenile great tits by simultaneously manipulating the density and sex ratio of fledglings within forest plots. We expected young females in the post-fledging period mainly to compete for resources related to food and, as they are subordinate to males, we predicted higher female dispersal from male-biased plots. Juvenile males compete for vacant territories already in late summer and autumn; thus, we predicted increased male dispersal from high density and male-biased plots. We found that juvenile females had a higher probability to leave male-biased plots and had dispersed further from male-biased plots in the later post-fledging phase when juvenile males start to become territorial and more aggressive. Juvenile males were least likely to leave male-biased plots and had smallest dispersal distances from female-biased plots early after fledging. The results suggest that the social environment differentially affected the costs and benefits of philopatry for male and female juveniles. The local sex ratio of individuals is thus an important social trait to be considered for understanding sex-specific dispersal processes

The Emergence of Emotions

Emotion is conscious experience. It is the affective aspect of consciousness. Emotion arises from sensory stimulation and is typically accompanied by physiological and behavioral changes in the body. Hence an emotion is a complex reaction pattern consisting of three components: a physiological component, a behavioral component, and an experiential (conscious) component. The reactions making up an emotion determine what the emotion will be recognized as. Three processes are involved in generating an emotion: (1) identification of the emotional significance of a sensory stimulus, (2) production of an affective state (emotion), and (3) regulation of the affective state. Two opposing systems in the brain (the reward and punishment systems) establish an affective value or valence (stimulus-reinforcement association) for sensory stimulation. This is process (1), the first step in the generation of an emotion. Development of stimulus-reinforcement associations (affective valence) serves as the basis for emotion expression (process 2), conditioned emotion learning acquisition and expression, memory consolidation, reinforcement-expectations, decision-making, coping responses, and social behavior. The amygdala is critical for the representation of stimulus-reinforcement associations (both reward and punishment-based) for these functions. Three distinct and separate architectural and functional areas of the prefrontal cortex (dorsolateral prefrontal cortex, orbitofrontal cortex, anterior cingulate cortex) are involved in the regulation of emotion (process 3). The regulation of emotion by the prefrontal cortex consists of a positive feedback interaction between the prefrontal cortex and the inferior parietal cortex resulting in the nonlinear emergence of emotion. This positive feedback and nonlinear emergence represents a type of working memory (focal attention) by which perception is reorganized and rerepresented, becoming explicit, functional, and conscious. The explicit emotion states arising may be involved in the production of voluntary new or novel intentional (adaptive) behavior, especially social behavior

Using long‐term data for a reintroduced population to empirically estimate future consequences of inbreeding

Inbreeding depression is an important long-term threat to reintroduced populations. However, the strength of inbreeding depression is difficult to estimate in wild populations because pedigree data are inevitably incomplete and because good data are needed on survival and reproduction. Predicting future population consequences is especially difficult because this also requires projecting future inbreeding levels and their impacts on long-term population dynamics, which are subject to many uncertainties. We illustrate how such projections can be derived through Bayesian state-space modeling methods based on a 26-year data set for North Island Robins (Petroica longipes) reintroduced to Tiritiri Matangi Island in 1992. We used pedigree data to model increases in the average inbreeding level (F) over time based on kinship of possible breeding pairs and to estimate empirically Ne/N (effective/census population size). We used multiple imputation to model the unknown components of inbreeding coefficients, which allowed us to estimate effects of inbreeding on survival for all 1458 birds in the data set while modeling density dependence and environmental stochasticity. This modeling indicated that inbreeding reduced juvenile survival (1.83 lethal equivalents [SE 0.81]) and may have reduced subsequent adult survival (0.44 lethal equivalents [0.81]) but had no apparent effect on numbers of fledglings produced. Average inbreeding level increased to 0.10 (SE 0.001) as the population grew from 33 (0.3) to 160 (6) individuals over the 25 years, giving a urn:x-wiley:08888892:media:cobi13646:cobi13646-math-0001 ratio of 0.56 (0.01). Based on a model that also incorporated habitat regeneration, the population was projected to reach a maximum of 331–1144 birds (median 726) in 2130, then to begin a slow decline. Without inbreeding, the population would be expected stabilize at 887–1465 birds (median 1131). Such analysis, therefore, makes it possible to empirically derive the information needed for rational decisions about inbreeding management while accounting for multiple sources of uncertainty