Vector activity and propagule size affect dispersal potential by vertebrates

Many small organisms in various life stages can be transported in the digestive system of larger vertebrates, a process known as endozoochory. Potential dispersal distances of these “propagules” are generally calculated after monitoring retrieval in experiments with resting vector animals. We argue that vectors in natural situations will be actively moving during effective transport rather than resting. We here test for the first time how physical activity of a vector animal might affect its dispersal efficiency. We compared digestive characteristics between swimming, wading (i.e. resting in water) and isolation (i.e. resting in a cage) mallards (Anas platyrhynchos). We fed plastic markers and aquatic gastropods, and monitored retrieval and survival of these propagules in the droppings over 24 h. Over a period of 5 h of swimming, mallards excreted 1.5 times more markers than when wading and 2.3 times more markers than isolation birds, the pattern being reversed over the subsequent period of monitoring where all birds were resting. Retention times of markers were shortened for approximately 1 h for swimming, and 0.5 h for wading birds. Shorter retention times imply higher survival of propagules at increased vector activity. However, digestive intensity measured directly by retrieval of snail shells was not a straightforward function of level of activity. Increased marker size had a negative effect on discharge rate. Our experiment indicates that previous estimates of propagule dispersal distances based on resting animals are overestimated, while propagule survival seems underestimated. These findings have implications for the dispersal of invasive species, meta-population structures and long distance colonization events.

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.

Solving the median shortest path problem in the planning and design of urban transportation networks using a vector labeling algorithm

This paper proposes an alternative algorithm to solve the median shortest path problem (MSPP) in the planning and design of urban transportation networks. The proposed vector labeling algorithm is based on the labeling of each node in terms of a multiple and conflicting vector of objectives which deletes cyclic, infeasible and extreme-dominated paths in the criteria space imposing cyclic break (CB), path cost constraint (PCC) and access cost parameter (ACP) respectively. The output of the algorithm is a set of Pareto optimal paths (POP) with an objective vector from predetermined origin to destination nodes. Thus, this paper formulates an algorithm to identify a non-inferior solution set of POP based on a non-dominated set of objective vectors that leaves the ultimate decision to decision-makers. A numerical experiment is conducted using an artificial transportation network in order to validate and compare results. Sensitivity analysis has shown that the proposed algorithm is more efficient and advantageous over existing solutions in terms of computing execution time and memory space used.

OVIE : object-oriented and vector-based image editing

A powerful image editing system called OVIE is described, which provides fast and accurate creation, composition, rendering and other manipulation of image contents. Flexibility and convenience of the system are achieved by including two modules: image decomposition and image vectorization to understand and represent an image respectively. To understand an image comprehensively, we propose to integrate image segmentation, shape completion and image completion techniques to ensure a seamless image editing. An array of pixels is replaced by vector data with geometric edit ability for image representation since the geometrically-based editing has physical meanings and thus it is more natural or intuitive for users to edit. Compared to the existing works, our system is more convenient and can generate effects with higher quality. © 2012 IEEE.

Subfossils of extinct and extant species of Simuliidae (Diptera) from Austral and Cook Islands (Polynesia): anthropogenic extirpation of an aquatic insect?

Subfossil head capsules of Simuliidae larvae have been recovered from swamps on Tubuai and Raivavae of the Austral Islands, and Atiu and Mangaia of the southern Cook Islands. For Tubuai and Raivavae it is likely that the simuliids are extinct, but a single simuliid species is extant on nearby Rurutu. For Atiu and Mangaia, extant simuliids have not been reported, but are known on Rarotonga. Well-preserved head capsules indicate that the Cook Islands subfossils are those of Simulium (Inseliellum) teruamanga Craig and Craig, 1986. For the Austral Islands, the simuliid from Tubuai is considered a variant of Simulium (Inseliellum) rurutuense Craig and Joy, 2000. That from Raivavae is morphologically distinct and is described here as a new species, Simulium (Inseliellum) raivavaense Craig and Porch. Humans arrived in Eastern Polynesia ca. 1,000 years ago resulting in the widespread destruction of lowland forest and conversion of wetlands to agriculture with implied consequences for the indigenous biota of these habitats. Here we consider that one such result was loss of freshwater aquatic biodiversity.

Sex differences in insect immune function : a consequence of diet choice?

Males often have reduced immune function compared to females but the proximate mechanisms underlying this taxonomically widespread pattern are unclear. Because immune function is resource-dependent and sexes may have different nutritional requirements, we hypothesized that sexual dimorphism in immune function may arise from differential nutrient intake (acquisition hypothesis). To test this hypothesis, we examined patterns of phenoloxidase (PO) activity in relation to nutrient consumption in Queensland fruit flies (Q-flies). In the first experiment, flies were allowed to choose their preferred nutrient intake. Compared with males, female Q-flies had higher PO activity, consumed more calories, and preferred a higher protein:carbohydrate (P:C) diet, suggesting that differential acquisition could explain sex differences. In the second experiment, we restricted flies to one of 12 diets varying in protein and carbohydrate concentrations and mapped PO activity for each sex onto a nutritional landscape. Counter to our hypothesis, females had higher PO activity than males at any given level of nutrient intake. Both carbohydrate and protein intake affected PO activity in females but only protein affected PO activity in males. Our results indicate that sex differences in Q-fly immune function are not solely explained by sex differences in nutrient intake, although nutrition does contribute to the magnitude of these sex differences.

A new approach based on support vector machine for solving stochastic optimization

Making decision usually occurs in the state of being uncertain. These kinds of problems often expresses in a formula as optimization problems. It is desire for decision makers to find a solution for optimization problems. Typically, solving optimization problems in uncertain environment is difficult. This paper proposes a new hybrid intelligent algorithm to solve a kind of stochastic optimization i.e. dependent chance programming (DCP) model. In order to speed up the solution process, we used support vector machine regression (SVM regression) to approximate chance functions which is the probability of a sequence of uncertain event occurs based on the training data generated by the stochastic simulation. The proposed algorithm consists of three steps: (1) generate data to estimate the objective function, (2) utilize SVM regression to reveal a trend hidden in the data (3) apply genetic algorithm (GA) based on SVM regression to obtain an estimation for the chance function. Numerical example is presented to show the ability of algorithm in terms of time-consuming and precision.