Inferring Size-Based Functional Responses From the Physical Properties of the Medium

First derivations of the functional response were mechanistic, but subsequent uses of these functions tended to be phenomenological. Further understanding of the mechanisms underpinning predator-prey relationships might lead to novel insights into functional response in natural systems. Because recent consideration of the physical properties of the environment has improved our understanding of predator-prey interactions, we advocate the use of physics-based approaches for the derivation of the functional response from first principles. These physical factors affect the functional response by constraining the ability of both predators and prey to move according to their size. A physics-based derivation of the functional response should thus consider the movement of organisms in relation to their physical environment. One recent article presents a model along these criteria. As an initial validation of our claim, we use a slightly modified version of this model to derive the classical parameters of the functional response (i.e., attack rate and handling time) of aquatic organisms, as affected by body size, buoyancy, water density and viscosity. We compared the predictions to relevant data. Our model provided good fit for most parameters, but failed to predict handling time. Remarkably, this is the only parameter whose derivation did not rely on physical principles. Parameters in the model were not estimated from observational data. Hence, systematic discrepancies between predictions and real data point immediately to errors in the model. An added benefit to functional response derivation from physical principles is thus to provide easy ways to validate or falsify hypotheses about predator-prey relationships.

Tolerance and functional response of Neoseiulus californicus (McGregaor) to starvation stress

The ability to endure starvation is important for predatory mite survival and its effectiveness as biological control agent. We studied the longevity and functional response of the prodator mite Neoseiulus californicus (McGregaor) under starvation stress. N. californicus individuals were treated as three groups: the mites were provided with nothing (group I), with fresh bean leaves (group II), and with water (group III) to the larvae, protonymphs, deutonymphs, adult males and females. Functional response of N. californicus to Tetranychus urticae Koch eggs was evaluated after starvation for 0 h, 24 h, 48 h, and 72 h. Results showed that all life stages of N. californicus in group III had the longest survival time among the three groups. Adult females had the strongest ability to endure starvation with a mean survival time of 8.16 d. Females had the ability to lay eggs, but the number of eggs laid was less than a mean of 1.2 eggs per female in the three starvation groups. N. californicus exhibited a Holling’s Type II functional response at all hunger levels. The handling time (Th) and attack rate (α) of the predator were not significantly different in all hunger levels. Predation rate of starved 0 h to 72 h initially increased and then decreased. The highest predation rate was recorded when the predators were starved for 24 h.

Artificial neural network tools for predicting the functional response of ultrafast laser textured/structured surfaces

Artificial Neural Networks (ANNs) are well-established knowledge acquisition systems with proven capacity for learning and generalisation. Therefore, ANNs are widely applied to solve engineering problems and are often used in laser-based manufacturing applications. There are different pattern recognition and control problems where ANNs can be effectively applied, and one of them is laser structuring/texturing for surface functionalisation, e.g. in generating Laser-Induced Periodic Surface Structures (LIPSS). They are a particular type of sub-micron structures that are very sensitive to changes in laser processing conditions due to processing disturbances like varying Focal Offset Distance (FOD) and/or Beam Incident Angle (BIA) during the laser processing of 3D surfaces. As a result, the functional response of LIPSS-treated surfaces might be affected, too, and typically needs to be analysed with time-consuming experimental tests. Also, there is a lack of sufficient process monitoring and quality control tools available for LIPSS-treated surfaces that could identify processing patterns and interdependences. These tools are needed to determine whether the LIPSS generation process is in control and consequently whether the surface’s functional performance is still retained. In this research, an ANN-based approach is proposed for predicting the functional response of ultrafast laser structured/textured surfaces. It was demonstrated that the processing disturbances affecting the LIPSS treatments can be classified, and then, the surface response, namely wettability, of processed surfaces can be predicted with a very high accuracy using the developed ANN tools for pre- and post-processing of LIPSS topography data, i.e. their areal surface roughness parameters. A Generative Adversarial Network (GAN) was applied as a pre-processing tool to significantly reduce the number of required experimental data. The number of areal surface roughness parameters needed to fully characterise the functional response of a surface was minimised using a combination of feature selection methods. Based on statistical analysis and evolutionary optimisation, these methods narrowed down the initial set of 21 elements to a group of 10 and 6 elements, according to redundancy and relevance criteria, respectively. The validation of ANN tools, using the salient surface parameters, yielded accuracy close to 85% when applied for identification of processing disturbances, while the wettability was predicted within an r.m.s. error of 11 degrees, equivalent to the static water contact angle (CA) measurement uncertainty.

Functional response in coccinellid beetles (Coleoptera: Coccinellidae) is modified by prey-density experience

In the present study, we assessed functional response curves of two generalist coccinellid beetles (Coleoptera: Coccinellidae), specifically Menochilus sexmaculatus and Propylea dissecta, using fluctuating densities of aphid prey as a stimulus. In what may be the first such study, we investigated how the prey density experienced during the early larval development of these two predatory beetle species shaped the functional response curves of the late instar–larval and adult stages. The predators were switched from their rearing prey-density environments of scarce, optimal, or abundant prey to five testing density environments of extremely scarce, scarce, suboptimal, optimal, or abundant prey. The individuals of M. sexmaculatus that were reared on either scarce- and optimal- or abundant-prey densities exhibited type II functional response curves as both larvae and adults. However, individuals of P. dissecta that were reared on scarce- and abundant-prey densities displayed modified type II functional response curves as larvae and type II functional response curves as adults. In contrast, individuals of P. dissecta reared on the optimal-prey density displayed type II functional response curves as larvae and modified type II functional response curves as adults. The fourth-instar larvae and adult females of M. sexmaculatus and P. dissecta also exhibited highest prey consumption (T/Th) and shortest prey-handling time (Th) on the scarce-prey rearing density. Thus, under fluctuating-prey conditions, M. sexmaculatus is a better biological control agent of aphids than P. dissecta is.