Effects of neighboring plants on herbivore damage to a focal plant (associational effects) have been documented in many systems and can lead to either increased or decreased herbivore attack. Mechanistic models that explain the observed variety of herbivore responses to local plant community composition have, however, been lacking. We present a model of herbivore responses to patches that consist of two plant types, where herbivore densities on a focal plant are determined by a combination of patch-finding, within-patch redistribution, and patch-leaving. Our analyses show that the effect of plant neighborhood on herbivores depends both on how plant and herbivore traits combine to affect herbivore movement and on how experimental designs reveal the effects of plant density and plant relative frequency. Associational susceptibility should be the dominant pattern when herbivores have biased landing rates within patches. Other behavioral decision rules lead to mixed responses, but a common pattern is that in mixed patches, one plant type experiences associational resistance while the other plant experiences associational susceptibility. In some cases, the associational effect may shift sign along a gradient of plant frequency, suggesting that future empirical studies should include more than two plant frequencies to detect nonlinearities. Finally, we find that associational susceptibility should be commonly observed in experiments using replacement designs, whereas associational resistance will be the dominant pattern when using additive designs. Consequently, outcomes from one experimental design cannot be directly compared to studies with other designs. Our model can also be translated to other systems with foragers searching for multiple resource types.

When searching for resources in heterogeneous environments, animals must rely on their abilities to detect the resources via their sensory systems. However, variation in the strength of the sensory cue may be mediated by the physical size of the resource patch. Patch detection of insects are often predicted by the scaling of sensory cues to patch size, where visual cues has been proposed to scale proportional to the diameter of the patch. The scaling properties of olfactory cues are, however, virtually unknown. Here, we investigated scaling rules for olfactory information in a gradient of numbers of odour sources, relevant to odour‐mediated attraction under field conditions. We recorded moth antennal responses to sex pheromones downwind from pheromone patches and estimated the slope in the scaling relationship between the effective length of the odour plumes and the number of odour sources. These measurements showed that the effective plume length increased proportional to the square root of the number of odour sources. The scaling relationship, as estimated in the field experiment, was then evaluated against field data of the slope in the relationship between trap catch and release rate of chemical attractants for a wide range of insects. This meta‐analysis revealed an average slope largely consistent with the estimated scaling relationship between the effective plume length and the number of odour sources. This study is the first to estimate the scaling properties of olfactory cues empirically and has implications for understanding and predicting the spatial distributions of insects searching by means of olfactory cues in heterogeneous environments.

Habitat area is an important predictor of spatial variation in animal densities. However, the area often correlates with the quantity of resources within habitats, complicating our understanding of the factors shaping animal distributions. We addressed this problem by investigating densities of insect herbivores in habitat patches with a constant area but varying numbers of plants. Using a mathematical model, predictions of scale-dependent immigration and emigration rates for insects into patches with different densities of host plants were derived. Moreover, a field experiment was conducted where the scaling properties of odour-mediated attraction in relation to the number of odour sources were estimated, in order to derive a prediction of immigration rates of olfactory searchers. The theoretical model predicted that we should expect immigration rates of contact and visual searchers to be determined by patch area, with a steep scaling coefficient, μ = -1. The field experiment suggested that olfactory searchers should show a less steep scaling coefficient, with μ ≈ -0.5. A parameter estimation and analysis of published data revealed a correspondence between observations and predictions, and density-variation among groups could largely be explained by search behaviour. Aphids showed scaling coefficients corresponding to the prediction for contact/visual searchers, whereas moths, flies and beetles corresponded to the prediction for olfactory searchers. As density responses varied considerably among groups, and variation could be explained by a certain trait, we conclude that a general theory of insect responses to habitat heterogeneity should be based on shared traits, rather than a general prediction for all species.

Roads may act as barriers to animal movements, but direct barrier effects on insects have rarely been studied. In this study we collected data on bees and wasps along two sides of a large road in Sweden using yellow pan traps. We then analyzed if the species composition differed between the two sides of the road; first for the whole community, and then only for the smallest species (which typically are poorer dispersers). As a complement, we analyzed if different vegetation variables differed between the two sides of the road, as this may also affect differences in species composition. Finally, we analyzed if species richness and abundance in general differed between the two sides and how these two response variables were explained by the vegetation variables. There was a significant difference in species composition between the eastern and the western side of the road when analyzing the whole community, and this relationship became even stronger when the largest species were excluded. The vegetation variables did not strongly differ between the two sides, and there was no difference in species richness and abundance of bees and wasps either. Abundance was, however, explained by the number of flowering plants in the surroundings of the trap. Even though using a rather limited data set, our results indicate that large roads may act as barriers on the movement of bees and wasps, especially for small species with poor dispersal ability. On the other hand, road verges may be important habitat for many species, which leads to a potential conflict that is important to consider in the planning of green infrastructure. RESEARCH ARTICLE Launched to accelerate biodiversity conservation A peer-reviewed open-access journalPetter Andersson et al. / Nature Conservation 18: 47-59 (2017) 48

Designing aircraft engines is a complex process in which requirements from multiple disciplines need to be considered. Decisions about product geometry and tolerances to achieve optimized aerodynamics, product life and weight can affect the manufacturing process. Therefore, providing information to designers about process capabilities is necessary to support design exploration and analysis. In this paper, the authors propose the Welding Capability Assessment Method (WCAM) as a tool to support the systematic identification and assessment of design issues related to product geometry critical to the welding process. Within this method, a list of potential failure modes during welding is connected to specific design parameters. Once the critical design parameters have been identified, quantitative methods are proposed to calculate tolerances to reduce the likelihood of welding failures. The application of this method is demonstrated through an industrial case study where a combination of interviews and welding simulations is used to study the welding capability of a number of product geometries. This method represents an advancement from traditional qualitative guidelines and expert judgments about welding difficulties towards a more quantitative approach, supporting virtual design.

In engineering, design analyses of complex products rely on computer simulated experiments. However, high-fidelity simulations can take significant time to compute. It is impractical to explore design space by only conducting simulations because of time constraints. Hence, surrogate modelling is used to approximate the original simulations. Since simulations are expensive to conduct, generally, the sample size is limited in aerospace engineering applications. This limited sample size, and also non-linearity and high dimensionality of data make it difficult to generate accurate and robust surrogate models. The aim of this paper is to explore the applicability of Random Forests (RF) to construct surrogate models to support design space exploration. RF generates meta-models or ensembles of decision trees, and it is capable of fitting highly nonlinear data given quite small samples. To investigate the applicability of RF, this paper presents an approach to construct surrogate models using RF. This approach includes hyperparameter tuning to improve the performance of the RF's model, to extract design parameters' importance and if-then rules from the RF's models for better understanding of design space. To demonstrate the approach using RF, quantitative experiments are conducted with datasets of Turbine Rear Structure use-case from an aerospace industry and results are presented.

This paper presents a prescriptive approach toward the integration of value and sustainability models in an automated decision support environment enabled by machine learning (ML). The approach allows the concurrent multidimensional analysis of design cases complementing mechanical simulation results with value and sustainability assessment. ML allows to deal with both qualitative and quantitative data and to create surrogate models for quicker design space exploration. The approach has been developed and preliminary implemented in collaboration with a major aerospace sub-system manufacturer.

Design requirements are often uncertain in the early stages of product development. Set-based design (SBD) is a paradigm for exploring, and keeping under consideration, several alternatives so that commitment to a single design can be delayed until requirements are settled. In addition, requirements may change over the lifetime of a component or a system. Novel manufacturing technologies may enable designs to be remanufactured to meet changed requirements. By considering this capability during the (SBD) optimization process, solutions can be scaled to meet evolving requirements and customer specifications even after commitment. Such an ability can also support a circular economy paradigm based on the return of used or discarded components and systems to working condition. We propose a (SBD) methodology to obtain scalable optimal solutions that can satisfy changing requirements through remanufacturing. We first use design optimization and surrogate modeling to obtain parametric optimal designs. This set of parametric optimal designs is then reduced to scalable optimal designs by observing a set of transition rules for the manufacturing process used (additive or subtractive). The methodology is demonstrated by means of a structural aeroengine component that is remanufactured by direct energy deposition of a stiffener to meet higher loading requirements