A comparison of plotless density estimators using Monte Carlo simulation on totally enumerated field data sets

Background: Plotless density estimators are those that are based on distance measures rather than counts per unit area (quadrats or plots) to estimate the density of some usually stationary event, e.g. burrow openings, damage to plant stems, etc. These estimators typically use distance measures between events and from random points to events to derive an estimate of density. The error and bias of these estimators for the various spatial patterns found in nature have been examined using simulated populations only. In this study we investigated eight plotless density estimators to determine which were robust across a wide range of data sets from fully mapped field sites. They covered a wide range of situations including animal damage to rice and corn, nest locations, active rodent burrows and distribution of plants. Monte Carlo simulations were applied to sample the data sets, and in all cases the error of the estimate (measured as relative root mean square error) was reduced with increasing sample size. The method of calculation and ease of use in the field were also used to judge the usefulness of the estimator. Estimators were evaluated in their original published forms, although the variable area transect (VAT) and ordered distance methods have been the subjects of optimization studies. Results: An estimator that was a compound of three basic distance estimators was found to be robust across all spatial patterns for sample sizes of 25 or greater. The same field methodology can be used either with the basic distance formula or the formula used with the Kendall-Moran estimator in which case a reduction in error may be gained for sample sizes less than 25, however, there is no improvement for larger sample sizes. The variable area transect (VAT) method performed moderately well, is easy to use in the field, and its calculations easy to undertake. Conclusion: Plotless density estimators can provide an estimate of density in situations where it would not be practical to layout a plot or quadrat and can in many cases reduce the workload in the field.

Lexicon for the Sensory Description of Australian Native Plant Foods and Ingredients

Understanding and describing Australian flavor has proved to be a challenge for marketers of native foods because of the diversity of unique flavor signatures exhibited. Descriptive analysis techniques were applied, using a panel of 11 experienced judges, to define and articulate the sensory properties of 18 key commercial Australian native plant foods and ingredients including fruits, herbs and spices. Quantitative descriptive data were transformed into concise and accurate verbal descriptions for each of the species. The sensory language developed during the vocabulary development panel sessions was combined, categorized and ordered to develop a sensory lexicon specific for the genre. The language developed to describe the foods and ingredients was diverse and distinctly Australian including aromas such as musk, rosella, citrus and spiced tea to eucalypt, bush scrub, fresh beetroot and wheat biscuit. Practical Applications This work provides a clear, useful means of characterizing and accurately describing the flavors of Australian native plant foods and ingredients. This information has been communicated to the native food industry, chefs, formulators, food technologists and flavor experts, and provides knowledge that will assist the wider food industry to successfully develop flavor blends and produce food products from native food ingredients. It is anticipated that extension of this information to both the local and international food markets will stimulate a renewed interest in Australian native ingredients and open new market opportunities for the industry. The data developed by this research have also formed the basis of quality control targets for emerging native foods and ingredients.