The foraging behaviour of the Black rat (Rattus rattus) within Australian macadamia orchards

The black rat (Rattus rattus) has been shown to be the primary species responsible for causing significant crop losses within the Australian macadamia industry. This species success within macadamia orchards is directly related to the flexibility expressed in its foraging behaviour. In this paper a conceptual foraging model is presented which proposes that the utilisation of resources by rodents within various components of the system is related not only to their relative abundance, but also to predator avoidance behaviour. Nut removal from high predation risk habitats during periods of low resource abundance in low risk compartments of the system is considered an essential behaviour that allows high rodent densities to be maintained throughout the year.

Predicting loss and fragmentation of habitat of the vulnerable subtropical rainforest tree Macadamia integrifolia with models developed from compiled ecological data

Habitat models are widely used in ecology, however there are relatively few studies of rare species, primarily because of a paucity of survey records and lack of robust means of assessing accuracy of modelled spatial predictions. We investigated the potential of compiled ecological data in developing habitat models for Macadamia integrifolia, a vulnerable mid-stratum tree endemic to lowland subtropical rainforests of southeast Queensland, Australia. We compared performance of two binomial models—Classification and Regression Trees (CART) and Generalised Additive Models (GAM)—with Maximum Entropy (MAXENT) models developed from (i) presence records and available absence data and (ii) developed using presence records and background data. The GAM model was the best performer across the range of evaluation measures employed, however all models were assessed as potentially useful for informing in situ conservation of M. integrifolia, A significant loss in the amount of M. integrifolia habitat has occurred (p < 0.05), with only 37% of former habitat (pre-clearing) remaining in 2003. Remnant patches are significantly smaller, have larger edge-to-area ratios and are more isolated from each other compared to pre-clearing configurations (p < 0.05). Whilst the network of suitable habitat patches is still largely intact, there are numerous smaller patches that are more isolated in the contemporary landscape compared with their connectedness before clearing. These results suggest that in situ conservation of M. integrifolia may be best achieved through a landscape approach that considers the relative contribution of small remnant habitat fragments to the species as a whole, as facilitating connectivity among the entire network of habitat patches.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

A unifying view of multiple kernel learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion’s dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.

A unifying view of multiple kernel learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying general optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion's dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

The rademacher complexity of coregularized kernel classes

In the multi-view approach to semisupervised learning, we choose one predictor from each of multiple hypothesis classes, and we co-regularize our choices by penalizing disagreement among the predictors on the unlabeled data. We examine the co-regularization method used in the co-regularized least squares (CoRLS) algorithm, in which the views are reproducing kernel Hilbert spaces (RKHS's), and the disagreement penalty is the average squared difference in predictions. The final predictor is the pointwise average of the predictors from each view. We call the set of predictors that can result from this procedure the co-regularized hypothesis class. Our main result is a tight bound on the Rademacher complexity of the co-regularized hypothesis class in terms of the kernel matrices of each RKHS. We find that the co-regularization reduces the Rademacher complexity by an amount that depends on the distance between the two views, as measured by a data dependent metric. We then use standard techniques to bound the gap between training error and test error for the CoRLS algorithm. Experimentally, we find that the amount of reduction in complexity introduced by co regularization correlates with the amount of improvement that co-regularization gives in the CoRLS algorithm.

The undirected feedback vertex set problem has a Poly(k) Kernel

Resolving a noted open problem, we show that the Undirected Feedback Vertex Set problem, parameterized by the size of the solution set of vertices, is in the parameterized complexity class Poly(k), that is, polynomial-time pre-processing is sufficient to reduce an initial problem instance (G, k) to a decision-equivalent simplified instance (G', k') where k' � k, and the number of vertices of G' is bounded by a polynomial function of k. Our main result shows an O(k11) kernelization bound.

Effects of cultivar on rodent damage in Australian macadamia orchards

The Black rat (Rattus rattus), a serious pest of Australian macadamia orchards has been estimated to cause up to 30% crop damage in Australian orchards. In recent years an increase in the number of commercially available cultivars has seen a change in orchard characteristics in Australia, primarily effecting fruiting and flowering patterns. This has been suggested to affect the feeding behaviour of rodents and in turn altered the damage process. In this study we compare the extent of damage in orchards containing one of three prevalent cultivars (A4/A16, A268 and HAES 344/741) and investigate the influence of these cultivars, particularly their distinctive fruiting traits, on rodent damage within the orchard. We demonstrate that the temporal pattern and extent of damage differs between cultivar types. Newer Australian macadamia cultivars tested in this study were found to be far more susceptible to rodent damage than the older Hawaiian developed cultivars, most likely due to an extended fruiting period and thinner shells. This has resulted in a more sustained period of crop damage than the patterns of crop damage observed in previous Australian studies. Crop damage caused by R. rattus is significantly higher in orchards that maintain high levels of canopy resources through the fruiting season and we postulate that this is due to the extended fruiting periods of the new cultivars used. The maintenance of canopy resource load in turn corresponds to high crop damage, in this study resulting in crop losses of up to 25%.

The spatial distribution of rodent damage in Australian macadamia (Macadamia integrifolia) orchards

The Black Rat (Rattus rattus), a global pest within the macadamia production industry, causes up to 30% crop damage in Australian orchards. During early stages of production in Australia, research demonstrated the importance of non crop adjacent habitats as significant in affecting the patterns of crop damage seen throughout orchards. Where once rodent damage was limited to the outside edges of orchard blocks, growers are now reporting finding crop damage throughout entire orchards. This study therefore aims to explore the spatial patterns of rodent distribution and damage now occurring in Australian macadamia orchards. We show that rodent damage and rodent distribution in these newer production regions differ from that shown in previous Australian research. Previous Australian research has shown damage patterns which were associated with the edges of orchard blocks however this study demonstrates a more widespread damage distribution. In the current study there is no relationship between rodent damage and the orchard edge. Arboreal rodent nests were identified within these newer orchard systems, suggesting rodents are residing within the tree component of the orchard system and not dependent on adjacent non-crop habitat for shelter. Results from this study confirm that rodents have modified their nesting and foraging behaviour in newer orchards systems in Australia. We suggest that this is a response of increased and prolonged availability of macadamia nuts in newer production regions enabling populations to be maintained throughout the year. Management strategies will require modification if control is to be achieved.

Kernel analysis on Grassmann manifolds for action recognition

Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds into reproducing kernel Hilbert spaces and then tackle the problem of discriminant analysis on such manifolds. To achieve efficient machinery, we propose graph-based local discriminant analysis that utilises within-class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, respectively. Experiments on KTH, UCF Sports, and Ballet datasets show that the proposed approach obtains marked improvements in discrimination accuracy in comparison to several state-of-the-art methods, such as the kernel version of affine hull image-set distance, tensor canonical correlation analysis, spatial-temporal words and hierarchy of discriminative space-time neighbourhood features.

Isoenergetic replacement of dietary saturated with monounsaturated fat via macadamia nuts enhances endothelial function in overweight subjects

Background & aims: - Excess adiposity (overweight) is one of numerous risk factors for cardiometabolic disease. Most risk reduction strategies for overweight rely on weight loss through dietary energy restriction. However, since the evidence base for long-term successful weight loss interventions is scant, it is important to identify strategies for risk reduction independent of weight loss. The aim of this study was to compare the effects of isoenergetic substitution of dietary saturated fat (SFA) with monounsaturated fat (MUFA) via macadamia nuts on coronary risk compared to usual diet in overweight adults. Methods: - A randomised controlled trial design, maintaining usual energy intake, but manipulating dietary lipid profile in a group of 64 (54 female, 10 male) overweight (BMI > 25), otherwise healthy, subjects. For the intervention group, energy intakes of usual (baseline) diets were calculated from multiple 3 day diet diaries, and SFA was replaced with MUFA (target: 50%E from fat as MUFA) by altering dietary SFA sources and adding macadamia nuts to the diet. Both control and intervention groups received advice on national guidelines for physical activity and adhered to the same protocol for diet diary record keeping and trial consultations. Anthropometric and clinical measures were taken at baseline and at 10 weeks. Results: A significant increase in brachial artery flow-mediated dilation (p < 0.05) was seen in the monounsaturated diet group at week 10 compared to baseline. This corresponded to significant decreases in waist circumference, total cholesterol (p < 0.05), plasma leptin and ICAM-1 (p < 0.01). Conclusions: - In patient subgroups where adherence to dietary energy-reduction is poor, isoenergetic interventions may improve endothelial function and other coronary risk factors without changes in body weight. This trial was registered with the Australia New Zealand Clinical Trial Registry (ACTRN12607000106437).