Ze Luo

The Survey of H5N1 Flu Virus in Wild Birds in 14 Provinces of China from 2004 to 2007

Background The highly pathogenic H5N1 avian influenza emerged in the year 1996 in Asia, and has spread to Europe and Africa recently. At present, effective monitoring and data analysis of H5N1 are not sufficient in Chinese mainland. Methodology/Principal Findings During the period from April of 2004 to August of 2007, we collected 14,472 wild bird samples covering 56 species of 10 orders in 14 provinces of China and monitored the prevalence of flu virus based on RT-PCR specific for H5N1 subtype. The 149 positive samples involved six orders. Anseriformes had the highest prevalence while Passeriformes had the lowest prevalence (2.70% versus 0.36%). Among the 24 positive species, mallard (Anas platyrhynchos) had the highest prevalence (4.37%). A difference of prevalence was found among 14 provinces. Qinghai had a higher prevalence than the other 13 provinces combined (3.88% versus 0.43%). The prevalence in three species in Qinghai province (Pintail (Anas acuta), Mallard (Anas platyrhynchos) and Tufted Duck (Aythya fuligula)) were obviously higher than those in other 13 provinces. The results of sequence analysis indicated that the 17 strains isolated from wild birds were distributed in five clades (2.3.1, 2.2, 2.5, 6, and 7), which suggested that genetic diversity existed among H5N1 viruses isolated from wild birds. The five isolates from Qinghai came from one clade (2.2) and had a short evolutionary distance with the isolates obtained from Qinghai in the year 2005. Conclusions/Significance We have measured the prevalence of H5N1 virus in 56 species of wild birds in 14 provinces of China. Continuous monitoring in the field should be carried out to know whether H5N1 virus can be maintained by wild birds.

Bird Migration and Risk for H5N1 Transmission into Qinghai Lake, China

The highly pathogenic avian influenza H5N1 virus still cause devastating effects to humans, agricultural poultry flocks, and wild birds. Wild birds are also detected to carry H5N1 over long distances and are able to introduce it into new areas during migration. In this article, our objective is to provide lists of bird species potentially involved in the introduction of highly pathogenic avian influenza H5N1 in Qinghai Lake, which is an important breeding and stopover site for aquatic birds along the Central Asian Flyway. Bird species were classified according to the following behavioral and ecological factors: migratory status, abundance, degree of mixing species and gregariousness, and the prevalence rate of H5N1 virus. Most of the high-risk species were from the family Anatidae, order Anseriformes (9/14 in spring, 11/15 in fall). We also estimated the relative risk of bird species involved by using a semi-quantitative method; species from family Anatidae accounted for over 39% and over 91% of the total risk at spring and fall migration periods, respectively. Results also show the relative risk for each bird aggregating site in helping to identify high-risk areas. This work may also be instructive and meaningful to the avian influenza surveillance in the breeding, stopover, and wintering sites besides Qinghai Lake along the Central Asian Flyway.

Exploring the wild birds’ migration data for the disease spread study of H5N1: a clustering and association approach

Knowledge about the wetland use of migratory bird species during the annual life circle is very interesting to biologists, as it is critically important in many decision-making processes such as for conservation site construction and avian influenza control. The raw data of the habitat areas and the migration routes are usually in large scale and with high complexity when they are determined by high-tech GPS satellite telemetry. In this paper, we convert these biological problems into computational studies and introduce efficient algorithms for the data analysis. Our key idea is the concept of hierarchical clustering for migration habitat localizations, and the notion of association rules for the discovery of migration routes from the scattered location points in the GIS. One of our clustering results is a tree structure, specially called spatial-tree, which is an illusive map depicting the breeding and wintering home range of bar-headed geese. A related result to this observation is an association pattern that reveals a high possibility that bar-headed geese’s potential autumn migration routes are likely between the breeding sites in the Qinghai Lake, China and the wintering sites in Tibet river valley. Given the susceptibility of geese to spread H5N1, and on the basis of the chronology and the rates of the bar-headed geese migration movements, we can conjecture that bar-headed geese play an important role in the spread of the H5N1 virus at a regional scale in Qinghai-Tibetan Plateau.

A new method for discovering behavior patterns among animal movements

ABSTRACT Advanced satellite tracking technologies enable biologists to track animal movements at fine spatial and temporal scales. The resultant data present opportunities and challenges for understanding animal behavioral mechanisms. In this paper, we develop a new method to elucidate animal movement patterns from tracking data. Here, we propose the notion of continuous behavior patterns as a concise representation of popular migration routes and underlying sequential behaviors during migration. Each stage in the pattern is characterized in terms of space (i.e., the places traversed during movements) and time (i.e. the time spent in those places); that is, the behavioral state corresponding to a stage is inferred according to the spatiotemporal and sequential context. Hence, the pattern may be interpreted predictably. We develop a candidate generation and refinement framework to derive all continuous behavior patterns from raw trajectories. In the framework, we first define the representative spots to denote the underlying potential behavioral states that are extracted from individual trajectories according to the similarity of relaxed continuous locations in certain distinct time intervals. We determine the common behaviors of multiple individuals according to the spatiotemporal proximity of representative spots and apply a projection-based extension approach to generate candidate sequential behavior sequences as candidate patterns. Finally, the candidate generation procedure is combined with a refinement procedure to derive continuous behavior patterns. We apply an ordered processing strategy to accelerate candidate refinement. The proposed patterns and discovery framework are evaluated through conceptual experiments on both real GPS-tracking and large synthetic datasets.

Mining Common Spatial-Temporal Periodic Patterns of Animal Movement

Advanced satellite tracking technologies enable biologists to track animal movements at finer spatial and temporal scales. The resulting long-term movement data is very meaningful for understanding animal activities. Periodic pattern analysis can provide insightful approach to reveal animal activity patterns. However, individual GPS data is usually incomplete and in limited lifespan. In addition, individual periodic behaviors are inherently complicated with many uncertainties. In this paper, we address the problem of mining periodic patterns of animal movements by combining multiple individuals with similar periodicities. We formally define the problem of mining common periodicity and propose a novel periodicity measure. We introduce the information entropy in the proposed measure to detect common period. Data incompleteness, noises, and ambiguity of individual periodicity are considered in our method. Furthermore, we mine multiple common periodic patterns by grouping periodic segments w.r.t. the detected period, and provide a visualization method of common periodic patterns by designing a cyclical filled line chart. To assess effectiveness of our proposed method, we provide an experimental study using a real GPS dataset collected on 29 birds in Qinghai Lake, China.

Mining continuous activity patterns from animal trajectory data

The increasing availability of animal tracking data brings us opportunities and challenges to intuitively understand the mechanisms of animal activities. In this paper, we aim to discover animal movement patterns from animal trajectory data. In particular, we propose a notion of continuous activity pattern as the concise representation of underlying similar spatio-temporal movements, and develop an extension and refinement framework to discover the patterns. We first preprocess the trajectories into significant semantic locations with time property. Then, we apply a projection-based approach to generate candidate patterns and refine them to generate true patterns. A sequence graph structure and a simple and effective processing strategy is further developed to reduce the computational overhead. The proposed approaches are extensively validated on both real GPS datasets and large synthetic datasets.

Application of Data Mining in Research of Avian Influenza Virus Cross-Species Infection

Avian Influenza Virus (AIV) has already crossed species barriers to infect humans, but the reason for avian flu cross-species infection is unknown. A lot of biology experiment accumulated tens of thousands of biological information data. As a new technology based on database and statistics, data mining provides unprecedented data analysis tool to biologists and powerful means to gene and proteins analysis and extraction. In this paper, we applied feature-based clustering and classification method to the research of AIV cross-species infection, finding useful patterns, and created a web-based early warning system. We also applied entropy plot and regression analysis to discover host-associated sites in AIV cross-species infection, and got the key sites that differentiate human versus avian influenza using the same method. Compared the two sets, we expected to discover some rules about the mutation trend.

A grid-based clustering algorithm for wild bird distribution

Advanced satellite tracking technologies provide biologists with long-term location sequence data to understand movement of wild birds then to find explicit correlation between dynamics of migratory birds and the spread of avian influenza. In this paper, we propose a hierarchical clustering algorithm based on a recursive grid partition and kernel density estimation (KDE) to hierarchically identify wild bird habitats with different densities. We hierarchically cluster the GPS data by taking into account the following observations: 1) the habitat variation on a variety of geospatial scales; 2) the spatial variation of the activity patterns of birds in different stages of the migration cycle. In addition, we measure the site fidelity of wild birds based on clustering. To assess effectiveness, we have evaluated our system using a large-scale GPS dataset collected from 59 birds over three years. As a result, our approach can identify the hierarchical habitats and distribution of wild birds more efficiently than several commonly used algorithms such as DBSCAN and DENCLUE.

Spatial Distribution Analysis of Wild Bird Migration in Qinghai Lake Based on Maximum Entropy Modeling

Species distribution analysis is becoming imperative in recent years. In this paper, we proposed the application of Maximum Entropy model to predict the species distribution from satellite tracking data and remote sensing data. We construct the 2-D geographic lattice to address the problem of huge calculating amounts resulting from the large amount GPS tracking records. The results of experiment showed the maxent model outperform the traditional classification in prediction, and the prediction results represented that the changing environment condition have directly impacts on wild species selection. We believe our method provide a useful step to understand the changing environment.

Investigating Home Range, Movement Pattern, and Habitat Selection of Bar-headed Geese during Breeding Season at Qinghai Lake, China

Simple Summary The Bar-headed Goose is an important species in Asia, both culturally and ecologically. While prior studies have shown Qinghai Lake supports one of the largest breeding areas for Bar-headed Geese, little is known regarding the species movement ecology during the breeding season. In this study, we examined Bar-headed Goose home range size within the breeding grounds at Qinghai Lake and documented their daily movement patterns and habitat selection. We also identified several key breeding sites surrounding Qinghai Lake. Our research provides valuable information on this sensitive species that could help develop the strategy for waterfowl conservation and disease control. Abstract The Bar-headed Goose is the only true goose species or Anserinae to migrate solely within the Central Asian Flyway, and thus, it is an ideal species for observing the effects of both land use and climate change throughout the flyway. In this paper, we investigate the home range, movement pattern, and habitat selection of Bar-headed Geese (Anser indicus) during the breeding season at Qinghai Lake, which is one of their largest breeding areas and a major migration staging area in the flyway. We identified several areas used by the geese during the breeding season along the shoreline of Qinghai Lake and found that most geese had more than one core use area and daily movements that provided insight into their breeding activity. We also observed the intensive use of specific wetlands and habitats near Qinghai Lake. These data provide interesting insights into the movement ecology of this important species and also provide critical information for managers seeking to understand and respond to conservation concerns threatening Bar-headed Geese, such as landscape and habitat changes.