Spatial assessment of the potential risk of avian influenza A virus infection in three raptor species in Japan

Moriguchi, Sachiko; Onuma, Manabu; Goka, Koichi

doi:10.1292/jvms.15-0551

Cited by 10 publications

(7 citation statements)

References 52 publications

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“…While there is limited evidence of disease transmission by migratory birds (McClure, 1974;Kim et al, 2012a;Moriguchi et al, 2016), their role as vectors and transporters of recently identified pathogens is largely unclear (Choi et al, 2015). More research on invasive species, known pathogens and other microorganisms carried by migratory landbirds will increase our knowledge of emerging diseases and protect animal and human health (World Health Organisation, 2017).…”

Section: Gaps In Understanding the Ecosystem Roles Of Migratory Landbmentioning

confidence: 99%

The State of Migratory Landbirds in the East Asian Flyway: Distributions, Threats, and Conservation Needs

Yong¹,

Heim

Chowdhury

et al. 2021

Front. Ecol. Evol.

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With nearly 400 migratory landbird species, the East Asian Flyway is the most diverse of the world’s flyways. This diversity is a consequence of the varied ecological niches provided by biomes ranging from broadleaf forests to arctic tundra and accentuated by complex biogeographic processes. The distribution and migration ecology of East Asian landbirds is still inadequately known, but a recent explosion in the number of studies tracking the migration of raptors, cuckoos, kingfishers and passerines has greatly increased our knowledge about the stopover and wintering ecology of many species, and the migratory routes that link northeast Eurasia and the Asian tropics. Yet the East Asian Flyway also supports the highest number of threatened species among flyways. Strong declines have been detected in buntings (Emberizidae) and other long-distance migrants. While the conservation of migratory landbirds in this region has largely focused on unsustainable hunting, there are other threats, such as habitat loss and increased agro-chemical use driven directly by land cover change and climate-related processes. Important knowledge gaps to be addressed include (1) threats affecting species in different parts of their annual cycle, (2) range-wide population trends, (3) ecological requirements and habitat use during the non-breeding season, and (4) the conservation status of critical wintering sites (including understudied farming landscapes, such as rice fields) and migration bottlenecks along the flyway.

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Section: Gaps In Understanding the Ecosystem Roles Of Migratory Landbmentioning

confidence: 99%

The State of Migratory Landbirds in the East Asian Flyway: Distributions, Threats, and Conservation Needs

Yong¹,

Heim

Chowdhury

et al. 2021

Front. Ecol. Evol.

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“…To minimize multi‐collinearity of environmental predictor variables, a PCA was carried out (Cruz‐Cárdenas, López‐Mata, Villaseñor, & Ortiz, ; Moriguchi, Onuma, & Goka, ) using SPSS 22.0. We used eigenvalues larger than 1.0 and the scree plot criterion or “broken stick” stopping rule for PCA in item level factoring (Bernstein, Garbin, & Teng, ).…”

Section: Methodsmentioning

confidence: 99%

High probability areas for ASF infection in China along the Russian and Korean borders

Fekede

Gils

Huang³

et al. 2019

Transbound Emerg Dis

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Summary African swine fever (ASF) is a transcontinental, contagious, fatal virus disease of pig with devastating socioeconomic impacts. Interaction between infected wild boar and domestic pig may spread the virus. The disease is spreading fast from the west of Eurasia towards ASF‐free China. Consequently, prediction of the distribution of ASF along the Sino‐Russian‐Korean borders is urgent. Our area of interest is Northeast China. The reported ASF‐locations in 11 contiguous countries from the Baltic to the Russian Federation were extracted from the archive of the World Organization for Animal Health from July 19, 2007 to March 27, 2017. The locational records of the wild boar were obtained from literature. The environmental predictor variables were downloaded from the WorldClim website. Spatial rarefication and pair‐wise geographic distance comparison were applied to minimize spatial autocorrelation of presence points. Principal component analysis (PCA) was used to minimize multi‐collinearity among predictor variables. We selected the maximum entropy algorithm for spatial modelling of ASF and wild boar separately, combined the wild boar prediction with the domestic pig census in a single map of suids and overlaid the ASF with the suids map. The accuracy of the models was assessed by the AUC. PCA delivered five components accounting for 95.7% of the variance. Spatial autocorrelation was shown to be insignificant for both ASF and wild boar records. The spatial models showed high mean AUC (0.92 and 0.97) combined with low standard deviations (0.003 and 0.006) for ASF and wild boar, respectively. The overlay of the ASF and suids maps suggests that a relatively short sector of the Sino‐Russian border has a high probability entry point of ASF at current conditions. Two sectors of the Sino‐Korean border present an elevated risk.

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“…In addition, the human population density from the Asia Continental Population Datasets (2000-2020), which are publicly and freely available both through the WorldPop Dataverse Repository and the WorldPop project website (http://www.worldpop.org.uk/data/), were used as predictor variables in this research. Principal component analysis (PCA) was used to reduce the number of continuous environmental variables [37,42,43]. During PCA, we used eigenvalues larger than 0.97 and the scree plot criterion for PCA in item level factoring [44].…”

Section: Environmental Variables and Preprocessingmentioning

confidence: 99%

Modeling the environmental suitability for Bacillus anthracis in the Qinghai Lake Basin, China

Arotolu

Wang

et al. 2022

PLoS ONE

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Bacillus anthracis is a gram-positive, rod-shaped and endospore-forming bacterium that causes anthrax, a deadly disease to livestock and, occasionally, to humans. The spores are extremely hardy and may remain viable for many years in soil. Previous studies have identified East Qinghai and neighbouring Gansu in northwest China as a potential source of anthrax infection. This study was carried out to identify conditions and areas in the Qinghai Lake basin that are environmentally suitable for B. anthracis distribution. Anthrax occurrence data from 2005–2016 and environmental variables were spatially modeled by a maximum entropy algorithm to evaluate the contribution of the variables to the distribution of B. anthracis. Principal Component Analysis and Variance Inflation Analysis were adopted to limit the number of environmental variables and minimize multicollinearity. Model performance was evaluated using AUC (area under the curve) ROC (receiver operating characteristics) curves. The three variables that contributed most to the suitability model for B. anthracis are a relatively high annual mean temperature of -2 to 0°C, (53%), soil type classified as; cambisols and kastanozems (35%), and a high human population density of 40 individuals per km2 (12%). The resulting distribution map identifies the permanently inhabited rim of the Qinghai Lake as highly suitable for B. anthracis. Our environmental suitability map and the identified variables provide the nature reserve managers and animal health authorities readily available information to devise both surveillance strategy and control strategy (administration of vaccine to livestock) in B. anthracis suitable regions to abate future epidemics.

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Spatial assessment of the potential risk of avian influenza A virus infection in three raptor species in Japan

Cited by 10 publications

References 52 publications

The State of Migratory Landbirds in the East Asian Flyway: Distributions, Threats, and Conservation Needs

The State of Migratory Landbirds in the East Asian Flyway: Distributions, Threats, and Conservation Needs

High probability areas for ASF infection in China along the Russian and Korean borders

Modeling the environmental suitability for Bacillus anthracis in the Qinghai Lake Basin, China

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