45 Yo Photos Mature

The chances of having a premature baby may be higher. Some studies have shown that men who are 45 or older have a 14% chance of having a baby born early. Men who are 50 years or older have a 28% chance of their newborn baby staying in the neonatal intensive care unit.

45 yo photos mature

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Mature-aged entrepreneurs run about a third of all businesses that are less than three years old. (All up, mature-aged entrepreneurs have started about 380,000 businesses with a turnover of about A$12 billion a year.)

Our research involved surveying more than 1,000 mature entrepreneurs and correlating the results to other studies on entrepreneurs. Our findings indicate older entrepreneurs have accumulated business and life experience, knowledge and skills, social networks and resources that better equip them for success. They tend to have better social skills and are better able to regulate their emotions than those younger.

This is a perfect representation of how Obama got elected. Favreau is representative of the demographic that the Obama camp pursued throughout the campaign. If you subtracted votes received from the immature, un-knowing, politically ignorant, facebook/youtube using youth in our society, McCain wins. Period.

Yes, Jon is 27 years old, there's a lot of young people in politics, especially in the Obama campaign. If he isn't a smart, mature, dependable, and a dang good speech writer then he wouldn't be on the President-Elect's team.

"I have so much respect for Seb Josseand his crew for the mature and professional way they have handledall the events that have arisen on this dramatic leg," saidJan Berent Heukensfeldt Jansen, CEO of Team ABN Amro. "Theseamanship displayed to recover Hans and then go to the rescueof fellow competitors is astounding. They are now continuingon to Portsmouth in the spirit of the race, as Hans would havewished."

Little is known about white-tailed deer (Odocoileus virginianus) vigilance and how social interactions and rank, breeding chronology, and other factors affect their anti-predator behaviors. White-tailed deer typically are sexually segregated throughout most of the year [25] with males forming loose aggregations consisting of different age-classes outside of the breeding season [26,27]. Older and larger males tend to be dominant over immature males and adult females [28,29]. Among mature males, dominance is more correlated with body mass than age [28]. In contrast, dominance among females is correlated with both increasing age [28] and body mass [30]. Nevertheless, large, mature males hold the highest social rank followed by adult females, immature males, and juveniles.

There is an inverse relationship between foraging and vigilance whereby the cost of vigilance is a decrease in foraging. Therefore, in order to better understand foraging-vigilance tradeoffs, we investigated factors influencing foraging behavior, specifically the probability of feeding while at a concentrated food resource. We hypothesized that each sex-age class would show unique responses to reproductive (phase of breeding season), social (presence of different sex-age class, group size), predation risk (distance-to-forest), and abiotic (time-of-day) cues. We predicted males would exhibit stronger responses to socio-sexual factors than adult females and juveniles. We predicted adult females would be most sensitive to predation risk during the pre-breeding season when their offspring were more vulnerable to predation [13,16]. We predicted feeding would be greater during diurnal periods [33] but decrease as distance-to-forest increased because of greater perceived risk associated with open areas. We predicted that all sex-age classes would perceive less individual predation risk when foraging in larger groups [7,33] and consequently increase feeding. Finally, we predicted subordinate sex-age classes would decrease feeding when in the presence of a mature male as vigilance would likely be directed at avoiding agonistic interactions with the more dominant sex-age class [13,15].

Predictor variables include standardized distance-to-forest, time-of-day (day = 1, night = 0), season (pre-breeding, breeding, post-breeding), presence of a mature male, presence of an immature male, presence of an adult female, presence of a juvenile, group size, year, feeder type, and feeding site. Each model included year, feeder type, and feeding site. Feeding site was treated as a random effect.

We included the interactions between distance-to-forest and time-of-day as well as distance-to-forest and season because perceived predation risk may change according to the diel cycle or as physiological condition changes relative to the breeding season. For the mature and immature male models, we included an interaction between their presence and season as the frequency of agonistic interactions between males is influenced by the phase of the breeding season [26], and these interactions may influence resource acquisition rates. Additionally, for the adult female and juvenile models, we included interaction terms for their presence in the respective models and season as females may decrease feeding rates when juveniles are present [13]. To control for potential differences in feeding rates influenced by year and feeder type, we included year and feeder type in each of our candidate models. We used Program R 3.1.2 for all statistical analyses [37].

Camera trapping effort was 375, 411, and 402 camera days for the pre-breeding, breeding, and post-breeding seasons, respectively. We collected 6,994 photographs containing images of 8,469 white-tailed deer for which we could assign a sex-age class. We recorded a total of 2,078 mature male, 2,479 immature male, 2,225 adult female, and 1,687 juvenile images.

Because of very few (n = 6) observations of mature males and adult females occurring in the same photograph, we failed to obtain convergence for the adult female and mature male models when we included their presence in the respective models. We also failed to obtain convergence for the mature male and immature male models when we included the seasonal interaction. Therefore, we removed those variables from the respective models.

Mature male feeding was best explained by model 1 that included distance-to-forest x time-of-day interaction, season x time-of-day interaction, presence of an immature male, presence of an adult female, presence of a juvenile, group size, year, feeder type, and feeding site (Table 2). Neither the distance-to-forest x time-of-day interaction nor the season x time-of-day interaction was significant. Feeding increased with increasing group size, decreased with increasing distance-to-forest, and was greatest during the breeding season (Table 3, Fig 1). The variance estimate for feeding site was 0.113.

a Predictor variables include standardized distance-to-forest (D), time-of-day (T), season (S), presence of a mature male (MM), presence of an immature male (IM), presence of an adult female (AF), presence of a juvenile (J), and group size (GS). Year, feeder type, and feeding site were included in all models. Feeding site was treated as a random effect.

Immature male feeding was best explained by models 1 and 3 (Table 2). The best-fitting models included distance-to-forest x time-of-day interaction, season x time-of-day interaction, presence of a mature male, presence of an adult female, presence of a juvenile, group size, year, feeder type, and feeding site. Neither the distance-to-forest x time-of-day interaction nor the season x time-of-day interaction was significant. Feeding increased with increasing group size, decreased when a mature male was present, and was greater during the pre-breeding and breeding seasons than the post-breeding season (Table 4, Fig 1). The variance estimate for feeding site was 0.034.

Adult female feeding was best explained by models 1, 3, and 4 (Table 2). The best-fitting models included distance-to-forest x time-of-day interaction, season x time-of-day interaction, presence of an immature male, presence of a juvenile x season interaction, group size, year, feeder type, and feeding site. None of the interactions were significant. Feeding increased with increasing group size and was greatest during the post-breeding season (Table 5, Fig 1). The variance estimate for feeding site was 0.017.

Juvenile feeding was best explained by model 4 (Table 2). The best-fitting model included presence of an immature male, presence of an adult female x season interaction, group size, year, feeder type, and feeding site. The presence of an adult female x season interaction was not significant. Feeding increased with increasing group size and when an adult female was present (Table 6). Juvenile feeding was not influenced by breeding chronology (Table 6, Fig 1). The variance estimate for feeding site was 0.032.

We observed marked temporal segregation at feeding sites between mature males and adult females. Several hypotheses have been proposed to explain sexual segregation in sexually-dimorphic ungulates including the predation risk and social factors hypotheses [25]. The predation risk hypothesis posits that sexes segregate due to differential predation risk based on body size, with larger males being less susceptible to predation. According to this hypothesis, males will exploit areas that pose a greater risk of encounters with predators [25]. An alternative hypothesis, the social factors hypothesis, proposes that sexes segregate to avoid aggressive interactions with the opposite sex [25]. In our study, all feeding sites were visited by mature males and adult females making spatial segregation an implausible explanation. It is possible that adult females avoided using the resource when mature males were present to reduce their interactions with behaviorally dominant mature males. 041b061a72