Design of Insect Target Tracking Algorithm in Clutter Based on the Multidimensional Feature Fusion Strategy

Entomological radar is an effective means of monitoring insect migration, and can realize long-distance and large-scale rapid monitoring. The stable tracking of individual insect targets is the basic premise underlying the identification of insect species and the study of insect migration mechanisms. However, the complex motion trajectory and large number of false measurements decrease the performance of insect target tracking. In this paper, an insect target tracking algorithm in clutter was designed based on the multidimensional feature fusion strategy (ITT-MFF). Firstly, multiple feature parameters of measurements were fused to calculate the membership of measurements and target, thereby improving the data association accuracy in the presence of clutter. Secondly, a distance-correction factor was introduced to the probabilistic data association (PDA) algorithm to accomplish multi-target data association with a low computational cost. Finally, simulation scenarios with different target numbers and clutter densities were constructed to verify the effectiveness of the proposed method. The tracking result comparisons of the experimental data acquired from a Ku-band entomological radar also indicate that the proposed method can effectively reduce computational cost while maintaining high tracking precision, and is suitable for engineering implementation.

A machine learning approach for classifying bird and insect radar echoes with S-band Polarimetric Weather Radar

The S-bandWSR-88D weather radar is sensitive enough to observe biological scatterers like birds and insects. However, their non-spherical shapes and frequent collocation in the radar resolution volume create challenges in identifying their echoes. We propose a method of extracting bird (or insect) features by coherently averaging dual polarization measurements from multiple radar scans, containing bird (insect) migration. Additional features are also computed to capture aspect and range dependence, and the variation of these echoes over local regions. Next, ridge classifier and decision tree machine learning algorithms are trained, first only with the averaged dual pol inputs and then different combinations of the remaining features are added. The performance of all models for both methods, are analyzed using metrics computed from the test data. Further studies on different patterns of birds/insects, including roosting birds, bird migration and insect migration cases, are used to further investigate the generality of our models. Overall, the ridge classifier using only dual polarization variables was found to perform consistently well across all these tests. Our recommendation is that this classifier can be used operationally on the US Next-Generation Radars (NEXRAD), as a first step in classifying biological echoes. It would be used in conjunction with the existing Hydrometeor Classification Algorithm (HCA), where the HCA would first separate biological from non-biological echoes, then our algorithm would be applied to further separate biological echoes into birds and insects. To the best of our knowledge, this study is the first to train a machine learning classifier that is capable of detecting diverse patterns of bird and insect echoes, based on dual polarization variables at each range gate.

The ‘migratory connectivity’ concept, and its applicability to insect migrants

Migratory connectivity describes the degree of linkage between different parts of an animal’s migratory range due to the movement trajectories of individuals. High connectivity occurs when individuals from one particular part of the migratory range move almost exclusively to another localized part of the migratory range with little mixing with individuals from other regions. Conversely, low migratory connectivity describes the situation where individuals spread over a wide area during migration and experience a large degree of mixing with individuals from elsewhere. The migratory connectivity concept is frequently applied to vertebrate migrants (especially birds), and it is highly relevant to conservation and management of populations. However, it is rarely employed in the insect migration literature, largely because much less is known about the migration circuits of most migratory insects than is known about birds. In this review, we discuss the applicability of the migratory connectivity concept to long-range insect migrations. In contrast to birds, insect migration circuits typically comprise multigenerational movements of geographically unstructured (non-discrete) populations between broad latitudinal zones. Also, compared to the faster-flying birds, the lower degree of control over movement directions would also tend to reduce connectivity in many insect migrants. Nonetheless, after taking account of these differences, we argue that the migratory connectivity framework can still be applied to insects, and we go on to consider postulated levels of connectivity in some of the most intensively studied insect migrants. We conclude that a greater understanding of insect migratory connectivity would be of value for conserving threatened species and managing pests.

Diversity, dynamics, direction, and magnitude of high-altitude migrating insects in the Sahel

Long-distance migration of insects impacts food security, public health, and conservation–issues that are especially significant in Africa. Windborne migration is a key strategy enabling exploitation of ephemeral havens such as the Sahel, however, its knowledge remains sparse. In this first cross-season investigation (3 years) of the aerial fauna over Africa, we sampled insects flying 40–290 m above ground in Mali, using nets mounted on tethered helium-filled balloons. Nearly half a million insects were caught, representing at least 100 families from thirteen orders. Control nets confirmed that the insects were captured at altitude. Thirteen ecologically and phylogenetically diverse species were studied in detail. Migration of all species peaked during the wet season every year across localities, suggesting regular migrations. Species differed in flight altitude, seasonality, and associated weather conditions. All taxa exhibited frequent flights on southerly winds, accounting for the recolonization of the Sahel from southern source populations. “Return” southward movement occurred in most taxa. Estimates of the seasonal number of migrants per species crossing Mali at latitude 14°N were in the trillions, and the nightly distances traversed reached hundreds of kilometers. The magnitude and diversity of windborne insect migration highlight its importance and impacts on Sahelian and neighboring ecosystems.

A Novel Radar Detection Method for Sensing Tiny and Maneuvering Insect Migrants

The use of radar to monitor insect migration is of great significance for pest control and biological migration mechanism research. However, migrating insects usually have small radar-cross-section (RCS) and are accompanied by maneuvering. The current radar detection algorithms mainly have contradictions in detection performance and computational complexity. So it is difficult for traditional radar detection algorithms to detect them effectively. Hence, a novel coherent integration detection algorithm based on dynamic programming (DP) and fractional Fourier transforming (FrFT) is proposed. By combining the advantages of DP and FrFT, the proposed DP-FrFT method can quickly search the target track, and simultaneously perform parameters estimation and motion compensation, achieving high integration gain with relatively low time consumption. The high efficiency of the method is verified with a large number of simulations and sufficient field experiments.

First evidence for an Amazonian insect migration in the butterfly Panacea prola (Lepidoptera: Nymphalidae)

ABSTRACTInsect migrations rival those of vertebrates in terms of numbers of migrating individuals and even biomass, although instances of the former are comparatively poorly documented. This is especially true in the world’s tropics, which harbor the vast majority of Earth’s insect species. Understanding these mass movements is of critical and increasing importance as global climate and land use change accelerate and interact to alter the environmental cues that underlie migration, particularly in the tropics. Here, we provide the first evidence for an insect migration for the nymphalid butterfly Panacea prola in the Amazon, the world’s largest and most biodiverse rainforest that is experiencing a shifting climate and rapid forest loss.

Change in geomagnetic field intensity alters migration-associated traits in a migratory insect

Geomagnetic field (GMF) intensity can be used by some animals to determine their position during migration. However, its role, if any, in mediating other migration-related phenotypes remains largely unknown. Here, we simulated variation in GMF intensity between two locations along the migration route of a nocturnal insect migrant, the brown planthopper Nilaparvata lugens , that varied by approximately 5 µT in field intensity. After one generation of exposure, we tested for changes in key morphological, behavioural and physiological traits related to migratory performance, including wing dimorphism, flight capacity and positive phototaxis. Our results showed that all three morphological and behavioural phenotypes responded to a small difference in magnetic field intensity. Consistent magnetic responses in the expression of the phototaxis-related Drosophila-like cryptochrome 1 ( Cry1 ) gene and levels of two primary energy substrates used during flight, triglyceride and trehalose, were also found. Our findings indicate changes in GMF intensity can alter the expression of phenotypes critical for insect migration and highlight the unique role of magnetoreception as a trait that may help migratory insects express potentially beneficial phenotypes in geographically variable environments.