Coastal marine habitat mapping using multi-beam acoustics, video data and a decision tree classifier on the temperate south-east Australian continental shelf

In this habitat mapping study, multi-beam acoustic data are integrated with extensive, precisely geo-referenced video validation data in a GIS environment to classify benthic substrates and biota at a 33km2 site in the near shore waters of Victoria, Australia. Using an automated decision-tree classification method, 5 representative biotic groups were identified in the Cape Nelson survey area using a combination of multi-beam bathymetry, backscatter and derivative products. Rigorous error assessment of derived, classified maps produced high overall accuracies (>85%) for all mapping products. In addition, a discrete multivariate analysis technique (kappa analysis) was used to assess classification accuracy. High-resolution (2.5m cell-size) representation of sea floor morphology and textural characteristics provided by multi-beam bathymetry and backscatter datasets, allowed the interpretation of benthic substrates of the Cape Nelson site and the communities of sessile organisms that populate them. Non-parametric multivariate statistical analysis (ANOSIM) revealed a significant difference in biotic composition between depth strata, and between substrate types. Incorporated with other descriptive measures, these results indicate that depth and substrate are important factors in the distributional ecology of the biotic communities at the Cape Nelson study site. BIOENV analysis indicates that derivatives of both multi-beam datasets (bathymetry and backscatter) are correlated with distribution and density of biotic communities. Results from this study provide new tools for research and management of the coastal zone.

Changing philosophies and tools for statistical inferences in behavioral ecology

Recent developments in ecological statistics have reached behavioral ecology, and an increasing number of studies now apply analytical tools that incorporate alternatives to the conventional null hypothesis testing based on significance levels. However, these approaches continue to receive mixed support in our field. Because our statistical choices can influence research design and the interpretation of data, there is a compelling case for reaching consensus on statistical philosophy and practice. Here, we provide a brief overview of the recently proposed approaches and open an online forum for future discussion (https://bestat.ecoinformatics.org/). From the perspective of practicing behavioral ecologists relying on either correlative or experimental data, we review the most relevant features of information theoretic approaches, Bayesian inference, and effect size statistics. We also discuss concerns about data quality, missing data, and repeatability. We emphasize the necessity of moving away from a heavy reliance on statistical significance while focusing attention on biological relevance and effect sizes, with the recognition that uncertainty is an inherent feature of biological data. Furthermore, we point to the importance of integrating previous knowledge in the current analysis, for which novel approaches offer a variety of tools. We note, however, that the drawbacks and benefits of these approaches have yet to be carefully examined in association with behavioral data. Therefore, we encourage a philosophical change in the interpretation of statistical outcomes, whereas we still retain a pluralistic perspective for making objective statistical choices given the uncertainties around different approaches in behavioral ecology. We provide recommendations on how these concepts could be made apparent in the presentation of statistical outputs in scientific papers.

Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association

Recognising behaviours of multiple people, especially high-level behaviours, is an important task in surveillance systems. When the reliable assignment of people to the set of observations is unavailable, this task becomes complicated. To solve this task, we present an approach, in which the hierarchical hidden Markov model (HHMM) is used for modeling the behaviour of each person and the joint probabilistic data association filters (JPDAF) is applied for data association. The main contributions of this paper lie in the integration of multiple HHMMs for recognising high-level behaviours of multiple people and the construction of the Rao-Blackwellised particle filters (RBPF) for approximate inference. Preliminary experimental results in a real environment show the robustness of our integrated method in behaviour recognition and its advantage over the use of Kalman filter in tracking people.

Examining the impact of reimbursement on referral to bone density testing for older adults: 8 years of data from the Barwon Statistical Division, Australia

Summary
In 2007, Medicare Australia revised reimbursement guidelines for dual energy X-ray absorptiometry (DXA) for Australians aged ≥70 years; we examined whether these changes increased DXA referrals in older adults. Proportions of DXA referrals doubled for men and tripled for women from 2003 to 2010; however, rates of utilization remained low.

Introduction
On April 1, 2007 Medicare Australia revised reimbursement guidelines for DXA for Australians aged ≥70 year; changes that were intended to increase the proportion of older adults being tested. We examined whether changes to reimbursement increased DXA referrals in older adults, and whether any sex differences in referrals were observed in the Barwon Statistical Division.

Methods
Proportions of DXA referrals 2003–2010 based on the population at risk ascertained from Australian Census data and annual referral rates and rate ratios stratified by sex, year of DXA, and 5-year age groups. Persons aged ≥70 years referred to the major public health service provider for DXA clinical purposes (n = 6,096; 21 % men).

Results
DXA referrals. Proportions of DXA referrals for men doubled from 0.8 % (2003) to 1.8 % (2010) and tripled from 2.0 to 6.3 % for women (all p < 0.001). For 2003–2006, referral ratios of men/women ranged between 1:1.9 and 1:3.0 and for 2007–2010 were 1:2.3 to 1:3.4. Referral ratios <2007:≥2007 were 1:1.7 for men aged 70–79 years (p < 0.001), 1:1.2 for men aged 80–84 years (p = 0.06), and 1:1.3 for men 85+ years (p = 0.16). For women, the ratios <2007:≥2007 were 1:2.1 (70–79 years), 1.1.5 (80–84 years), and 1:1.4 (85+ years) (all p < 0.001).

Conclusions
DXA referral ratios were 1:1.6 (men) and 1:1.8 (women) for 2007–2010 vs. 2003–2006; proportions of referrals doubled for men and tripled for women from 2003 to 2010. Overall, rates of DXA utilization remained low. Policy changes may have had minimal influence on referral; thus, ongoing evaluation over time is warranted.

Power, effects, confidence, and significance: an investigation of statistical practices in nursing research

Objectives: To (a) assess the statistical power of nursing research to detect small, medium, and large effect sizes; (b) estimate the experiment-wise Type I error rate in these studies; and (c) assess the extent to which (i) a priori power analyses, (ii) effect sizes (and interpretations thereof), and (iii) confidence intervals were reported. Design: Statistical review. Data sources: Papers published in the 2011 volumes of the 10 highest ranked nursing journals, based on their 5-year impact factors. Review methods: Papers were assessed for statistical power, control of experiment-wise Type I error, reporting of a priori power analyses, reporting and interpretation of effect sizes, and reporting of confidence intervals. The analyses were based on 333 papers, from which 10,337 inferential statistics were identified. Results: The median power to detect small, medium, and large effect sizes was .40 (interquartile range [. IQR]. = .24-.71), .98 (IQR= .85-1.00), and 1.00 (IQR= 1.00-1.00), respectively. The median experiment-wise Type I error rate was .54 (IQR= .26-.80). A priori power analyses were reported in 28% of papers. Effect sizes were routinely reported for Spearman's rank correlations (100% of papers in which this test was used), Poisson regressions (100%), odds ratios (100%), Kendall's tau correlations (100%), Pearson's correlations (99%), logistic regressions (98%), structural equation modelling/confirmatory factor analyses/path analyses (97%), and linear regressions (83%), but were reported less often for two-proportion z tests (50%), analyses of variance/analyses of covariance/multivariate analyses of variance (18%), t tests (8%), Wilcoxon's tests (8%), Chi-squared tests (8%), and Fisher's exact tests (7%), and not reported for sign tests, Friedman's tests, McNemar's tests, multi-level models, and Kruskal-Wallis tests. Effect sizes were infrequently interpreted. Confidence intervals were reported in 28% of papers. Conclusion: The use, reporting, and interpretation of inferential statistics in nursing research need substantial improvement. Most importantly, researchers should abandon the misleading practice of interpreting the results from inferential tests based solely on whether they are statistically significant (or not) and, instead, focus on reporting and interpreting effect sizes, confidence intervals, and significance levels. Nursing researchers also need to conduct and report a priori power analyses, and to address the issue of Type I experiment-wise error inflation in their studies. © 2013 .

Design and interpretation of anthropometric and fitness testing of basketball players

The volume of literature on fitness testing in court sports such as basketball is considerably less than for field sports or individual sports such as running and cycling. Team sport performance is dependent upon a diverse range of qualities including size, fitness, sport-specific skills, team tactics, and psychological attributes. The game of basketball has evolved to have a high priority on body size and physical fitness by coaches and players. A player's size has a large influence on the position in the team, while the high-intensity, intermittent nature of the physical demands requires players to have a high level of fitness. Basketball coaches and sport scientists often use a battery of sport-specific physical tests to evaluate body size and composition, and aerobic fitness and power. This testing may be used to track changes within athletes over time to evaluate the effectiveness of training programmes or screen players for selection. Sports science research is establishing typical (or 'reference') values for both within-athlete changes and between-athlete differences. Newer statistical approaches such as magnitude-based inferences have emerged that are providing more meaningful interpretation of fitness testing results in the field for coaches and athletes. Careful selection and implementation of tests, and more pertinent interpretation of data, will enhance the value of fitness testing in high-level basketball programmes. This article presents reference values of fitness and body size in basketball players, and identifies practical methods of interpreting changes within players and differences between players beyond the null-hypothesis.