Social influences on the estrous cycle of the captive sun bear (Helarctos Malayanus)

Adolescence and the development of social behaviour in giraffes

All mammals experience different life stages as they develop, each of which is characterised by particular physical and behavioural changes. Despite the emergence of sophisticated behaviour analysis techniques, the ways in which social behaviour varies by life stage, and how this is influenced by an individual’s sex, is relatively understudied in most social mammals other than primates and elephants. Understanding the social requirements of mammals should be a central and critical component to their conservation, captive management and welfare. Here, we apply social network analysis techniques to understand how social behaviour differs with life stage in the giraffe, a gregarious fission–fusion mammal. We studied two wild populations of giraffes in Kenya and found that adolescents have significantly stronger associations with adolescents of their own sex first and foremost, then adults of their own sex. Other associations were significantly lower than would be expected, or non-significant. Our results suggest that adolescence in both male and female giraffes shares similar features to adolescence in other social mammal species. We discuss how the application of such knowledge might improve the management and welfare of captive giraffes.

The Bias of combining variables on fish's aggressive behavior studies

Quantifying animal aggressive behavior by behavioral units, either displays or attacks, is a common practice in animal behavior studies. However, this practice can generate a bias in data analysis, especially when the variables have different temporal patterns. This study aims to use Bayesian Hierarchical Linear Models (B-HLMs) to analyze the feasibility of pooling the aggressive behavior variables of four cichlids species. Additionally, this paper discusses the feasibility of combining variables by examining the usage of different sample sizes and family distributions to aggressive behaviour variables. The subject species were: the angelfish (Pterophyllum scalare), the tiger oscar (Astronotus ocellatus), the Cichlasoma paranaense and the Nile tilapia (Oreochromis niloticus). For each species, 15 groups of 3 individuals were assigned to daily observations (10-min recordings) for 5 days. Aggressive behavior data was labeled according to its aggressive intensity. The variables chase (C), tail beating (TB), push (P), lateral attack (LA) and bite (B) were classified as high intensity. The variables undulation (U), lateral threat (LT) and frontal displays (FD) were classified as low intensity. These behaviors, however, were not present in all species. Model parameters were estimated by Monte Carlo Markov chains using non-informative priors. B-HLMs were performed to assess the impact probability of each variable in the analysis. Results revealed that when combining variables, the resulting distribution is strongly influenced by only one variable in each category. Moreover, in some cases the aggregate values altered the results, which changed the probabilities of the main variables. Species with low aggressive behavior frequencies, such as A. ocellatus, are more sensitive to this bias. LT was the main low intensity variable for all species, while B was the main high intensity variable for the P. scalare and the O. niloticus. LA was the high intensity category variable that was the most relevant for the C. paranaense and A. ocellatus. Moreover, combining the variables did not impact the feasibility of reducing the sample size when compared to using the most quantitative variable. For all species a sample size of 12 did not change the study conclusions. With respect to family distribution, based on DIC values the Gaussian model is more suitable for most of the studied species. However, caution should be taken, because the Gaussian posterior probability distribution overlapped 0 in some cases, which is biologically impossible in aggressive behaviors. The only exception is the A. ocellatus, which, based on DIC values, was the only species better modeled by a Poisson distribution. Bayesian analysis can be therefore considered a strong tool for analyzing aggressive behavior.

Serial transrectal ultrasonography for monitoring the reproductive activity of the Asiatic black bear (Ursus thibetanus ussuricus)

This study evaluated the structural changes in the reproductive tract of Asiatic black bears using serial transrectal ultrasonography. In addition, the ultrasonographic observations were compared with the results of vaginal cytology and hormonal analyses. The collection of blood for hormonal analysis, vaginal cytology and transrectal ultrasonography was performed in two bears (Bears 1 and 2) from June 2011 to August 2013 without mating and in a third bear (Bear 3) from April to December 2012, allowing natural mating. Serial ultrasonographic observations showed cyclic changes in ovarian structures (e.g. emergence of small follicles, growth and ovulation of dominant follicles and corpus luteum (CL) formation) during the reproductive cycles of the three bears. The diameter of the uterine horns remained similar throughout the reproductive cycle in Bears 1 and 2, and it remained similar from April until October, but an enlargement containing foetuses was observed in Bear 3 in December. The ultrasonographic observations were consistent with the data obtained through vaginal cytology and progesterone analysis during the reproductive cycle. An average of 4.0 (±0.4) dominant follicles was observed during the oestrous stage (May-August), during which the superficial cells accounted for >90% of the total vaginal cells. In addition, the detection of an average of 2.6 (±0.2) CL was associated with increased plasma progesterone concentrations (3.0 ± 0.4 ng/ml) between June and December (near hibernation). In conclusion, serial transrectal ultrasonography demonstrated yearly oestrous (ovulation) cycles via follicular dynamics and CL formation on ovaries, accordingly with vaginal cytology and hormonal level in the Asiatic black bear.