Loitering with intent-Catching the outlier vessels at sea.

Illegal, Unreported and Unregulated (IUU) fishing activities pose one of the most significant threats to sustainable fisheries worldwide. Identifying illegal behaviour, specifically fishing and at-sea transhipment, continues to be a fundamental hurdle in combating IUU fishing. Here, we explore the use of spatial statistical methods to identify vessels behaving anomalously, in particular with regard to loitering, using the Indonesian Exclusive Economic Zone (EEZ) and surrounding waters as a case-study. Using Automatic Identification System (AIS) for vessel tracking, we applied Generalized Additive Models to capture both the temporal and spatial nature of loitering behaviour. We identified three statistically anomalous loitering behaviours (based on time, speed and distance) and applied the models to 2700 vessels in the region. We were able to rank vessels for individual and joint probability of atypical behaviour, providing a hierarchical list of vessels engaging in anomalous behaviour. While identification of irregular behaviour does not mean vessels are definitely engaging in illegal activities, this statistical modelling approach can be used to prioritise the allocation of enforcement resources and assist authorities under the United Nations Food and Agricultural Organization Port State Measures Agreement for management and enforcement of IUU fishing associated activities.

Detecting suspicious activities at sea based on anomalies in Automatic Identification Systems transmissions.

Automatic Identification Systems (AIS) are a standard feature of ocean-going vessels, designed to allow vessels to notify each other of their position and route, to reduce collisions. Increasingly, the system is being used to monitor vessels remotely, particularly with the advent of satellite receivers. One fundamental problem with AIS transmission is the issue of gaps in transmissions. Gaps occur for three basic reasons: 1) saturation of the system in locations with high vessel density; 2) poor quality transmissions due to equipment on the vessel or receiver; and 3) intentional disabling of AIS transmitters. Resolving which of these mechanisms is responsible for generating gaps in transmissions from a given vessel is a critical task in using AIS to remotely monitor vessels. Moreover, separating saturation and equipment issues from intentional disabling is a key issue, as intentional disabling is a useful risk factor in predicting illicit behaviors such as illegal fishing. We describe a spatial statistical model developed to identify gaps in AIS transmission, which allows calculation of the probability that a given gap is due to intentional disabling. The model we developed successfully identifies high risk gaps in the test case example in the Arafura Sea. Simulations support that the model is sensitive to frequent gaps as short as one hour. Results in this case study area indicate expected high risk vessels were ranked highly for risk of intentional disabling of AIS transmitters. We discuss our findings in the context of improving enforcement opportunities to reduce illicit activities at sea.

Prospective study of physical activity and depressive symptoms in middle-aged women.

BACKGROUND: Although many studies support an inverse association between physical activity (PA) and depressive symptoms, prospective relationships between these variables have been confounded by pre-existing psychological and physical health problems. METHODS: This study examined the dose-response relationships between self-reported PA and depressive symptoms, using cross-sectional and prospective data from a population-based cohort of middle-aged women who participated in the Australian Longitudinal Study on Women's Health (ALSWH) between 1996 and 2001. Participants completed three mailed surveys (S1, 1996; S2, 1998; S3, 2001), which included questions about time spent in walking, moderate- and vigorous-intensity PA, and measures of psychological health (Center for Epidemiologic Studies Depression scale [CESD-10], and Mental health [MH] subscale of the Short Form 36 survey). Relationships between previous (S1, S2), current (S3), and habitual (S1, S2, S3) PA and "depressive symptoms" were examined, adjusting for sociodemographic and health-related variables (n=9207). RESULTS: Mean CESD-10 scores decreased, and MH scores increased with increasing levels of previous, current, and habitual activity. Odds ratios for CESD-10 scores > or =10 or MH scores < or =52 at S3 were 30% to 40% lower among women who reported the equivalent of > or =60 minutes of moderate-intensity PA per week, compared with those who reported less PA than this. Women who were in the lowest PA category at S1, but who subsequently reported > or =240 metabolic equivalent minutes (MET.mins) per week had lower odds of CESD-10 scores of > or =10 or MH scores < or =52 at S3 than those who remained in the very low PA category. CONCLUSIONS: These data suggest that there is a clear relationship between increasing PA and decreasing depressive symptoms in middle-aged women, independent of pre-existing physical and psychological health.

Relations between companion animals and self-reported health in older women: cause, effect or artifact?

A large longitudinal dataset on women's health in Australia provided the basis of analysis of potential positive health effects of living with a companion animal. Age, living arrangements, and housing all strongly related to both living with companion animals and health. Methodological problems in using data from observational studies to disentangle a potential association in the presence of substantial effects of demographic characteristics are highlighted. Our findings may help to explain some inconsistencies and contradictions in the literature about the health benefits of companion animals, as well as offer suggestions for ways to move forward in future investigations of human-pet relationships.

Identifying the energy gap: magnitude and determinants of 5-year weight gain in midage women.

OBJECTIVE: The aims of this study were to estimate average yearly weight gain in midage women and to identify the determinants of weight gain and gaining weight at double the average rate. RESEARCH METHODS AND PROCEDURES: The study sample comprised 8071 participants (45 to 55 years old) in the Australian Longitudinal Study on Women's Health who completed mailed surveys in 1996, 1998, and 2001. RESULTS: On average, the women gained almost 0.5 kg per year [average 2.42 kg (95% confidence interval, 2.29 to 2.54) over 5 years]. In multivariate analyses, variables associated with energy balance (physical activity, sitting time, and energy intake), as well as quitting smoking, menopause/hysterectomy, and baseline BMI category were significantly associated with weight gain, but other behavioral and demographic characteristics were not. After adjustment for all of the other biological and behavioral variables, the odds of gaining weight at about twice the average rate (>5 kg over 5 years) were highest for women who quit smoking (odds ratio = 2.94; 95% confidence interval, 2.17, 3.96). There were also independent relationships between the odds of gaining >5 kg and lower levels of habitual physical activity, more time spent sitting, energy intake (but only in women with BMI > 25 at baseline), menopause transition, and hysterectomy. DISCUSSION: The average weight gain equates with an energy imbalance of only about 10 kcal or 40 kJ per day, which suggests that small sustained changes in the modifiable behavioral variables could prevent further weight gain.