DEVELOPMENT OF A ‘BIPOLAR’ R‐INDEX1

A new ‘bipolar’ R‐index analysis was proposed and evaluated. Eighteen judges evaluated red color in eight wine samples by comparing each sample with the control. Judges indicated whether the sample had ‘more’, the 'same’, or ‘less’ red color than the control, and whether they were sure or unsure of their decision. Three computational methods were used to examine the results: the ‘traditional’ R‐index, the ‘bipolar’ R‐index (R_more or R_less) and the ‘weighted‐bipolar’ R‐index. While all three methods provided consistent results, the ‘bipolar’ R‐indices reflected bidirectional differences among the samples thus providing more information. A refinement to the computation (‘weighted‐bipolar’ R‐index) was an approach for eliminating the bias associated with overestimation of the sample size and accordingly changed some of the significance levels. Further research is currently underway to expand the scope and application of this method.     

Spatial model for predicting the presence of cinnamon fungus (Phytophthora cinnamomi) in sclerophyll vegetation communities in south‐eastern Australia

Abstract The pathogen Phytophthora cinnamomi causes extensive ‘dieback’ of Australian native vegetation. This study investigated the distribution of infection in an area of significant sclerophyll vegetation in Australia. It aimed to determine the relationship of infection to site variables and to develop a predictive model of infection. Site variables recorded at 50 study sites included aspect, slope, altitude, proximity to road and road characteristics, soil profile characteristics and vegetation attributes. Soil and plant tissues were assayed for the presence of the pathogen. A geographical information systyem (GIS) was employed to provide accurate estimations of spatial variables and develop a predictive model for the distribution of P. cinnamomi. The pathogen was isolated from 76% of the study sites. Of the 17 site variables initially investigated during the study a logistic regression model identified only two, elevation and sun‐index, as significant in determining the probability of infection. The presence of P. cinnamomi infection was negatively associated with elevation and positively associated with sun‐index. The model predicted that up to 74% of the study area (11 875 ha) had a high probability of being affected by P. cinnamomi. However, the present areas of infection were small, providing an opportunity for management to minimize spread into highly susceptible uninvaded areas.     

SENSORY SHELF-LIFE ESTIMATION OF ALFAJOR BY SURVIVAL ANALYSIS

Survival analysis methodology was used to estimate the shelf life of alfajor (a chocolate‐coated individually wrapped cake) at 20 and 35C by using results obtained from consumers when asked if they would accept or reject samples with different storage times. Sensory acceptability (measured by consumers), off‐flavor (measured by a trained panel) and moisture content were linearly related to time. These correlations were used to estimate values at the shelf‐life times calculated for 25 and 50% rejection probability. Survival analysis provided the following shelf‐life estimation: 74 days at 20C and 33 days at 35C for a 25% of rejection, 87 days at 20C and 39 days at 35C for a 50% of rejection. An alfajor stored at 20C having an acceptability value below 4.9 (1–9 hedonic scale) and off‐flavor intensity above 5.3 (0–10 scale) would be rejected by 25% of the consumers. Chemical data were not good shelf‐life predictors.     

Variation in wetland invertebrate communities in lowland acidic fens and swamps

1. Invertebrates were collected semi‐quantitatively from four relatively undisturbed wetlands in the west coast of New Zealand’s South Island: two acidic fens and two swamps. Samples were collected from up to four discrete habitats within each wetland: large open‐water channels, small leads (small, ill‐defined channels with emergent vegetation in them) and large (>10 m diameter) or small (<10 m diameter) ponds. Samples were also collected from different plant species within each wetland, each with different morphology, and from areas without vegetation. This was done to determine whether invertebrate communities varied more between‐wetlands than within‐wetlands, as the results had implications for future wetland monitoring programmes. 2. Principal components analysis of water chemistry data revealed striking differences in pH, conductivity and nutrients between the four wetlands. Not surprisingly, pH was lowest in one of the acidic fens, and highest in one of the swamps, where conductivity was also high. Midges (Tanytarsus, Tanypodinae, Orthocladiinae and Ceratopogonidae), nematodes, harpactacoid copepods and the damselfly Xanthocnemis dominated the invertebrate fauna. Orthoclad midges and mites were the most widespread taxa, found in 91 of 94 samples. Diptera were the most diverse invertebrate group, followed by Trichoptera and Crustacea. 3. Ordination analysis of the invertebrate data showed that the four wetlands supported different invertebrate communities. However, species composition did not change completely along the ordination axes, suggesting that a relatively species‐poor invertebrate fauna was found in the wetlands. Taxa such as molluscs were restricted to wetlands with high pH. Multi‐response permutation procedures (MRPP) was used to analyse resultant ordination scores to see how they differed according to five terms: ‘Wetland’, ‘Habitat’, ‘Growth Form’, ‘Morphology’ and ‘Plant’. Most of the sample separation along ordination axes reflected differences between wetland, although the ‘Habitat’ and ‘Plant’ terms also explained some of the variation. The ‘Growth Form’ and ‘Morphology’ terms had only minor effects on community composition. 4. A multivariate regression tree modelled invertebrate assemblages according to the five predictor terms. The resultant model explained 54.8% of the species variance. The ‘Wetland’ term contributed most to the explanatory power, followed by ‘Habitat’. ‘Growth type’ and ‘Morphology’ explained only a small amount of variance to the regression tree, while the different plant species explained none of the variation. 5. Variation in these New Zealand wetland invertebrate communities appears to be controlled most by large‐scale factors operating at the level of individual wetlands, although different habitats within individual wetlands contributed slightly to this variation. Based on these results, sampling programmes to describe wetland invertebrate communities do not need to sample specific habitats or plant types within a wetland. Instead, samples can be collected from a wide range of habitats within individual wetlands, and pooled. Within each habitat, it is unnecessary to collect individual samples from different macrophytes or un‐vegetated areas. Our results suggest that collecting replicate pooled samples from different habitats within each wetland will be sufficient to characterize the invertebrate assemblage of each wetland.     

VOLATILE FLAVOR COMPOUNDS AFFECTING CONSUMER ACCEPTABILITY OF KIWIFRUIT

Sensory research on kiwifruit has shown that specific sugars and acids, and volatile flavor compounds have causative effects on "sweet" and "acid" aroma and flavor attributes. New Zealand consumers (n = 162) assessed the perceived flavor intensity and acceptability of three volatile flavor compounds (ethyl butanoate, E-2-hexenal and hexanal) at varying levels in a model base solution. Increasing levels of ethyl butanoate positively affected all the acceptability attributes ("overall liking,""liking of aroma,""liking of flavor") as well as increased the perceived intensity of "kiwifruit aroma" and "kiwifruit flavor." Increasing levels of E-2-hexenal negatively affected all the acceptability attributes but increased the perceived intensity of "kiwifruit aroma" and "acid flavor." An increase in hexanal increased perceived intensity of "kiwifruit aroma." Ethyl butanoate and E-2-hexenel at the levels tested had the most prominent effects on consumer perceived intensity and acceptability of the "kiwifruit flavor."     

INFLUENCE OF EDUCATION/PROFESSION OF MEXICAN CONSUMERS ON ACCEPTANCE AND PURCHASE INTENT OF CORN TORTILLA

Influence of education/profession of Mexican consumers on acceptance and purchase intent of corn tortillas was evaluated. Two groups of 150 Mexican consumers each (faculty/graduate students [A] versus field laborers [B]) rated sensory acceptability of corn tortillas. Acceptance and purchase intent were rated using a binomial scale. Group B had higher expectations towards corn tortillas as indicated by lower acceptability ratings. Both groups used rollability, resistance-to-tearing, and chewiness to differentiate tortilla samples, but group B paid more attention to these attributes as indicated by higher canonical correlation values. Acceptance was influenced by overall liking for both groups; chewiness and taste were also significant for group B. Purchase intent was influenced by overall liking and taste for both groups, but color for group A, and appearance and chewiness for group B were also critical. This study demonstrated that education/profession of Mexican consumers affected their acceptance and purchase intent of corn tortillas.

PRACTICAL APPLICATIONS

This study revealed that education/profession of Mexican consumers affected acceptance and purchase intent of corn tortillas. Two groups of consumers with different levels of education had different expectation toward tortillas when making purchase decision. Understanding how each consumer segment differently perceives about the tortilla product will give marketers and manufacturers a better direction for developing tortilla products with expected sensory qualities.     

Interpreting the 'selection effect' of biodiversity on ecosystem function

Experimental ecosystems often function differently than expected under the null hypothesis that intra‐ and interspecific interactions are identical. Recent theory attributes this to the ‘selection effect’ (dominance by species with particular traits), and the ‘complementarity effect’ (niche differentiation and/or facilitative interactions). Using the Price Equation, I show that the ‘selection effect’ only partially reflects dominance by species with particular traits at the expense of other species, and therefore is only partially analogous to natural selection. I then derive a new, tripartite partition of the difference between observed and expected ecosystem function. The ‘dominance effect’ is analogous to natural selection. ‘Trait‐independent complementarity’ occurs when species function better than expected, independent of their traits and not at the expense of other species. ‘Trait‐dependent complementarity’ occurs when species with particular traits function better than expected, but not at the expense of other species. I illustrate the application of this new partition using experimental data.     

Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble‐based methods?

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble‐based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30‐day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1999–2001 and 2004–2005). We found that both the in‐sample and out‐of‐sample prediction of ensemble methods offered substantial improvement in predicting cardiovascular mortality compared to conventional regression trees. However, conventional logistic regression models that incorporated restricted cubic smoothing splines had even better performance. We conclude that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short‐term mortality in population‐based samples of subjects with cardiovascular disease.     

Spatio-temporal co-occurrence of cougars (Felis concolor), wolves (Canis lupus) and their prey during winter: a comparison of two analytical methods

Aims To examine the spatio‐temporal co‐occurrence of cougars (Felis concolor), wolves (Canis lupus), and their prey during winter using monthly (November–March) species–environment relationship models. In addition, to contrast predictions across two methods: logistic regression and Geographic Information System (GIS) image correlation. Location The eastern front ranges of the Canadian Rocky Mountains (south‐central Alberta), approximately 100 km west of Calgary, including portions of Banff National Park and Kananaskis Country. Methods Snow‐tracking data were collected simultaneously for cougars, wolves, elk (Cervus elaphus), and deer (Odocoileus virginianus and O. hemionus) between November and March, 1997–2000. Track data were synthesized in a GIS. Logistic regression and Akaike's information criterion (AIC) were used to select optimal environmental relationship models for each species. We first examined co‐occurrence by iteratively using each species as a dependent variable (presence/absence) in a logistic regression analysis and using all other species track‐density estimates as independent variables. We built predictive surfaces in a GIS using the exponent form of the logistic regression models, and assessed model accuracy with a receiver operating characteristic curve. We then re‐examined co‐occurrence using pairwise correlations of species probability surfaces by month. The correlation results were compared with logistic regression results to illuminate mechanisms of co‐occurrence and to investigate predictive consistency across the two methods. Results Cougars showed a trend in distribution from higher elevation and less rugged terrain in December, to lower elevation and more rugged terrain in March. This trend differed from that for wolves, which showed a more stable affinity for low elevation and less rugged valley bottoms across all months. The logistic regression models indicated variable positive and negative associations of cougars with wolves by month, and changes in prey associations over time. Notably, there was a shift in co‐occurrence for both predators from elk to deer in March. We found high predictive accuracy for all probability surfaces, except for the month of January. Our image comparison showed that spatial co‐occurrence amongst all species increased over winter, except that wolves and cougars were negatively correlated in February. Combining the results of each approach we found that cougars and wolves converged spatially over winter at the landscape scale (i.e. the valley), while showing more discrete use of that space over time and by habitat attributes (e.g. forest cover, topographic complexity, and prey track density). Main conclusions In the Rocky Mountains, the spatial distributions of cougars and wolves converged into the valley floor as winter progressed. Cougars were distinct from wolves and prey in the intensity of this shift. We determined that a comparison of predictive surfaces alone fails to explain species co‐occurrence. The surfaces must be coupled with investigation of respective species–environment models to account for temporal changes in associations. We suggest that the two approaches represent different ecological scales: image comparison may be best for landscape‐ (valley) level analysis, while logistic regression is best for site‐level analysis. Ultimately, both approaches were critical to our analysis. Finally, the variability observed over time suggested that annual and seasonal models may obscure important ecological patterns and processes, especially for cougars.     

DEVELOPMENT OF A LEXICON FOR BEEF FLAVOR IN INTACT MUSCLE

ABSTRACT

A lexicon describing the flavor characteristics of beef across different cuts, grades, and cooking temperatures and methods was developed. Four major cuts of the United States Department of Agriculture (USDA) quality grade beef were cooked to five endpoint temperatures using braising, broiling (oven broiling and electric charbroiling), roasting and grilling (indoor and outdoor grilling). Six highly trained panelists identified and defined a total of 38 aroma and flavor characteristics in 176 beef samples. Beef identity, brown/roasted, bloody/serumy, metallic, fat‐like, overall sweet, sour aromatics and five tastes were present in practically all samples. Other attributes were present only in certain samples, depending on either the sample group or the cooking method/endpoint temperature combination used. This lexicon potentially offers the beef industry a standard tool to identify and quantify flavor attributes as impacted by temperature, cooking method, aging process, storage time, diet regime, packaging, USDA quality grades, etc.

PRACTICAL APPLICATIONS

Until now, the beef industry's main focus has been to assess beef tenderness and juiciness, with an emphasis on ways to improve beef texture. Meat companies and academic institutions have been using the 1995 American Meat Science Association guidelines to assess the flavor of beef, which are not comprehensive. Recent work has focused on flavor, and the industry needs a standardized flavor lexicon that can be used for many projects. It is important for the industry to be able to systematically identify and quantify flavor attributes that drive consumer acceptance.     

Geographical,environmental and intrinsic biotic controls on Phanerozoic marine diversification

Abstract: The Paleobiology Database now includes enough data on fossil collections to produce useful time series of geographical and environmental variables in addition to a robust global Phanerozoic marine diversity curve. The curve is produced by a new ‘shareholder quorum’ method of sampling standardization that removes biases but avoids overcompensating for them by imposing entirely uniform data quotas. It involves drawing fossil collections until the taxa that have been sampled at least once (the ‘shareholders’) have a summed total of frequencies (i.e. coverage) that meets a target (the ‘quorum’). Coverage of each interval’s entire data set is estimated prior to subsampling using a variant of a standard index, Good’s u. This variant employs counts of occurrences of taxa described in only one publication instead of taxa found in only one collection. Each taxon’s frequency within an interval is multiplied by the interval’s index value, which limits the maximum possible sampling level and thereby creates the need for subsampling. Analyses focus on a global diversity curve and curves for northern, southern and ‘tropical’ (30°N to 30°S) palaeolatitudinal belts. Tropical genus richness is remarkably static, so most large shifts in the curve reflect trends at higher latitudes. Changes in diversity are analysed as a function of standing diversity; the number, spacing and palaeolatitudinal position of sampled geographical cells; the mean onshore–offshore position of cells; and proportions of cells from carbonate, onshore and reefal environments. Redundancy among the variables is eliminated by performing a principal components analysis of each data set and using the axis scores in multiple regressions. The key factors are standing diversity and the dominance of onshore environments such as reefs. These factors combine to produce logistic growth patterns with slowly changing equilibrium values. There is no evidence of unregulated exponential growth across any long stretch of the Phanerozoic, and in particular there was no large Cenozoic radiation beyond the Eocene. The end‐Ordovician, Permo–Triassic and Cretaceous–Palaeogene mass extinctions had relatively short‐term albeit severe effects. However, reef collapse was involved in these events and also may have caused large, longer term global diversity decreases in the mid‐Devonian and across the Triassic/Jurassic boundary. Conversely, the expansion of reef ecosystems may explain newly recognized major radiations in the mid‐Permian and mid‐Jurassic. Reef ecosystems are particularly vulnerable to current environmental disturbances such as ocean acidification, and their decimation might prolong the recovery from today’s mass extinction by millions or even tens of millions of years.     

Univariate Dose-Response Models in Case-Control Studies

For modelling dose-response relationships in case-control studies the multiplicative logistic regression model, assuming the relative risk to be an exponential function of the dose, is widely known. If the relative risk is assumed to be a linear function of the dose, several authors (see e.g. BERRY (1980)) have proposed an additive (linear) model. This model has a better fit with the data if such a linear relation holds. Confidence limits for the relative risk derived from the information matrix, however, appear to be rather inaccurate. Therefore, use of the ‘standard’ logistic model in two different ways was studied: extension with a quadratic term or a logarithmic transformation of the dose. By applying the methods both to an empirical data set and in a simulation experiment, it is shown that appropriate transformation (often logarithmic) of the dosage and then applying the ‘standard’ logistic model is an useful approach if a linear dose-response relationship holds.