Distance software: design and analysis of distance sampling surveys for estimating population size

1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance. 2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use. 3. Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated. 4. A first step in analysis of distance sampling data is modeling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple-covariate distance sampling, which allows covariates in addition to distance; and mark–recapture distance sampling, which relaxes the assumption of certain detection at zero distance. 5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap. 6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modeling analysis engine for spatial and habitat-modeling, and information about accessing the analysis engines directly from other software. 7. Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of- the-art software that implements these methods is described that makes the methods accessible to practicing ecologists.

Food Handling Practices, Knowledge and Beliefs of Families with Young Children Based on the Health Belief Model

Objective: To determine current food handling practices, knowledge and beliefs of primary food handlers with children 10 years old and the relationship between these components. Design: Surveys were developed based on FightBac!™ concepts and the Health Belief Model (HBM) construct. Participants: The majority of participants (n= 503) were females (67%), Caucasians (80%), aged between 30 to 49 years old (83%), had one or two children (83%), prepared meals all or most of the time (76%) and consumed meals away from home three times or less per week (66%). Analysis: Descriptive statistics and inferential statistics using Spearman’s rank correlation coefficient (rho) (p<0.05 and one-tail) and Chi-square were used to examine frequency and correlations. Results: Few participants reached the food safety objectives of Healthy People 2010 for safe food handling practices (79%). Mixed results were reported for perceived susceptibility. Only half of the participants (53-54%) reported high perceived severity for their children if they contracted food borne illness. Most participants were confident of their food handling practices for their children (91%) and would change their food handling practices if they or their family members previously experienced food poisoning (79%). Participants’ reasons for high self-efficacy were learning from their family and independently acquiring knowledge and skills from the media, internet or job. The three main barriers to safe food handling were insufficient time, lots of distractions and lack of control of the food handling practices of other people in the household. Participants preferred to use food safety information that is easy to understand, has scientific facts, causes feelings of health-threat and has lots of pictures or visuals. Participants demonstrate high levels of knowledge in certain areas of the FightBac!TM concepts but lacked knowledge in other areas. Knowledge and cues to action were most supportive of the HBM construct, while perceived susceptibility was least supportive of the HBM construct. Conclusion: Most participants demonstrate many areas to improve in their food handling practices, knowledge and beliefs. Adviser: Julie A. Albrecht