Chapter Six – Individual Trait Variation and Diversity in Food Webs

In recent years, there has been a renewed interest in the ecological consequences of individual trait variation within populations. Given that individual variability arises from evolutionary dynamics, to fully understand eco-evolutionary feedback loops, we need to pay special attention to how standing trait variability affects ecological dynamics. There is mounting empirical evidence that intra-specific phenotypic variation can exceed species-level means, but theoretical models of multi-trophic species coexistence typically neglect individual-level trait variability. What is needed are multispecies datasets that are resolved at the individual level that can be used to discriminate among alternative models of resource selection and species coexistence in food webs. Here, using one the largest individual-based datasets of a food web compiled to date, along with an individual trait-based stochastic model that incorporates Approximate Bayesian computation methods, we document intra-population variation in the strength of prey selection by different classes or predator phenotypes which could potentially alter the diversity and coexistence patterns of food webs. In particular, we found that strongly connected individual predators preferentially consumed common prey, whereas weakly connected predators preferentially selected rare prey. Such patterns suggest that food web diversity may be governed by the distribution of predator connectivity and individual trait variation in prey selection. We discuss the consequences of intra-specific variation in prey selection to assess fitness differences among predator classes (or phenotypes) and track longer term food web patterns of coexistence accounting for several phenotypes within each prey and predator species.

The examination and identification of bite marks in foods using 3D scanning and 3D comparison methods

Bite mark analysis offers the opportunity to identify the biter based on the individual characteristics of the dentitions. Normally, the main focus is on analysing bite mark injuries on human bodies, but also, bite marks in food may play an important role in the forensic investigation of a crime. This study presents a comparison of simulated bite marks in different kinds of food with the dentitions of the presumed biter. Bite marks were produced by six adults in slices of buttered bread, apples, different kinds of Swiss chocolate and Swiss cheese. The time-lapse influence of the bite mark in food, under room temperature conditions, was also examined. For the documentation of the bite marks and the dentitions of the biters, 3D optical surface scanning technology was used. The comparison was performed using two different software packages: the ATOS modelling and analysing software and the 3D studio max animation software. The ATOS software enables an automatic computation of the deviation between the two meshes. In the present study, the bite marks and the dentitions were compared, as well as the meshes of each bite mark which were recorded in the different stages of time lapse. In the 3D studio max software, the act of biting was animated to compare the dentitions with the bite mark. The examined food recorded the individual characteristics of the dentitions very well. In all cases, the biter could be identified, and the dentitions of the other presumed biters could be excluded. The influence of the time lapse on the food depends on the kind of food and is shown on the diagrams. However, the identification of the biter could still be performed after a period of time, based on the recorded individual characteristics of the dentitions.

Food Volume Computation for Self Dietary Assessment Applications

There is great demand for easily-accessible, user-friendly dietary self-management applications. Yet accurate, fully-automatic estimation of nutritional intake using computer vision methods remains an open research problem. One key element of this problem is the volume estimation, which can be computed from 3D models obtained using multi-view geometry. The paper presents a computational system for volume estimation based on the processing of two meal images. A 3D model of the served meal is reconstructed using the acquired images and the volume is computed from the shape. The algorithm was tested on food models (dummy foods) with known volume and on real served food. Volume accuracy was in the order of 90 %, while the total execution time was below 15 seconds per image pair. The proposed system combines simple and computational affordable methods for 3D reconstruction, remained stable throughout the experiments, operates in near real time, and places minimum constraints on users.