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This paper examines the role of the World Organisation for Animal Health (WOAH) in the global surveillance and management of pathogens. Since the creation of WOAH, one of its missions has been to ensure transparency of the global animal health situation. WOAH established a Working Group on Wildlife in 1994 to inform and advise WOAH Members, leadership, and technical teams on issues relating to wildlife health. In 2020 it conducted a consultation with its Members before developing a Wildlife Health Framework to improve global health and wildlife conservation. WOAH Members report diseases in wildlife, but detections are dependent on the surveillance systems in place. As an example of data collected in the most recent years (2019–2023), 154 countries have reported 68,862,973 cases, through alert messages and weekly updates, for 84 diseases. One-hundred and fifty countries have reported 68,672,115 cases in domestic animals and 95 countries have reported 190,858 cases in wild animals. These figures illustrate the performance of the organization in collecting data on wildlife, and provide an indication of the difference in completeness of data collected in domestic animals and wildlife. There are several challenges to wildlife disease surveillance and real figures remain unknown; they depend on the existence, quality and sensitivity of national surveillance. A WOAH-led One Health approach with cross-sectoral collaboration is needed to improve surveillance sensitivity, address the challenges and help safeguard wildlife population health and biodiversity conservation.

The vulnerability and flood risk assessment of Bhitarkanika National Park in Odisha, India, was conducted using a data-driven approach and a machine-based embedded decision support system. The park, located in the estuaries of the Brahmani, Baitarani, Dharma, and Mahanadi river systems, is home to India’s second-largest mangrove environment and the world’s most active and diverse saline wetland. To evaluate its vulnerability and risk, various threats were considered, with a focus on floods. Satellite imageries, such as Landsat 8 OLI, SRTM digital elevation model, open street map, Google pro image, reference map, field survey, and other ancillary data, were utilized to develop vulnerability and risk indicators. These indicators were then reclassified into ‘Cost’ and ‘Benefit’ categories for better understanding. The factors were standardized using the max-min standardization method before being fed into the vulnerability and risk model. Initially, an analytical hierarchy approach was used to develop the model, which was later compared with machine learning algorithms (e.g., SVM) and uncertainty analysis indices (e.g., overall accuracy, kappa, map quality, etc.). The results showed that the SVM-RBF machine learning algorithm outperformed the traditional geostatistical model (AHP), with an overall accuracy of 99.54% for flood risk mapping compared to AHP’s 91.12%. The final output reveals that a large area of Bhitarkanika National park falls under high flood risk zone. The Eastern coastal regions of Govindapur, Kanhupur, Chinchri, Gobardhanpur and Barunei fall under high risk zone of tidal floods, The Northern and western regions of Ramachandrapur, Jaganathpur, Kamalpur, Subarnapur, Paramanandapur, etc., Fall under high risk region of riverine floods. The study also revealed that the areas covered with mangroves have a higher elevation and hence are repellent to any kind of flood. In the event of a flood high priority conservation measures should be taken along all high flood risk areas. This study is helpful for decision-making and carrying out programs for the conservation of natural resources and flood management in the national park and reserve forest for ecological sustainability to support sustainable development goals (e.g., SDGs-14, 15).

Intense urbanization in the Indian cities has brought about phenomenal changes in the existing landuse patterns, whereby increasing trends of surface temperature and the presence and absence of green cover and water bodies, their shape, pattern, configuration significantly modifies the thermal properties of the city. The present study comprehendeds how different biophysical, anthropogenic, and landscape composition determine and interact with the surface temperature in different climatic zones in India, by taking Jaipur and Guwahati cities as examples. This study has also incorporated several landscape metrics of composition and configuration and spatial cluster analyses to identify the most fragmented landscape in the two cities. Such complex relationship among the landuse and landcover, bio-physical parameters and the surface temperature can be assessed by using the Generalised Additive Model (GAM). Result shows that Jaipur has experienced high temperature changes in city centre in comparison to Guwahati, which can be attributed to the differential presence of environmental sensitive land class in the city cores and peripheries. Results also indicate that medium patches of environmental sensitive land class, as well as close proximity and configuration with environmental non-sensitive class reduces the surface temperature in two heterogenous landscape. Thus, Policymakers and urban planners could be assisted by our finding to mitigate surface temperature in the city.

There is growing concern, how and to what extent future changes in climate will affect human society and natural environments. Continuous emissions of Green House Gasses (GHGs) at or above current rates will cause further warming. This, in turn, may modify global climate system during 21st century that very likely would have larger impacts than those observed during 20th century. At present, Global Climate Models (GCMs) are only the most reliable tools available for studying behaviour of the climate system. This paper presents a comprehensive review of GCMs including their development and applications in climate change impacts studies. Following a discussion of the limitations of GCMs at regional and local scales, different approaches of downscaling are discussed in detail.

Sutlej basin, a mountainous river basin is located in N-W Himalayan region. This basin has highest potential for hydropower generation as compared to other basins of Indus River system. Recent studies have revealed rise in mean annual surface temperature which will modify pattern of Sutlej River flow in this basin. The present paper has aimed for studying annual and seasonal patterns of river discharge at different gauging sites of Sutlej River basin (middle catchment), India. The study has been performed over three gauging sites, namely, Kasol, Sunni and Rampur located under different physiographic and climatic conditions. The daily historical records (1970-2010) of 41 years river discharge data have been employed for statistical analysis. The annual and seasonal Standardized Discharge Indices (SDI) has been derived in order to preserve uniformity and facilitate comparison between flows of Sutlej River at different sites. Mann-Kendall (MK) test, a non-parametric test method, has been applied to detect trend in annual and seasonal SDI for periods 1970-2010. Decadal (annual and seasonal) patterns in SDI have also been discussed. The results of annual and seasonal trend analysis have revealed decreasing trends in SDI at all the gauging sites. The trend in annual SDI is statistically significant (95% confidence level) at Rampur (0,04 cumec/year) and insignificant at Kasol (0,02 cumec/year) and Sunni (0,01 cumec/year) respectively. The study of annual decadal change in SDI at all the sites shows that reduction in river discharge has occurred in the decade of 2001-2010. Before this, continuous rise in annual discharge has been reported at all the sites from decades 1970-1980 to the last decade of 20th century (1991-2000). The decline in river flow may affect agriculture and electricity production as well as there may be problems related with drinking water. The present study is expected to be useful for planning water resources related projects that can be undertaken in the Sutlej basin.

Vector or reservoir species of three diseases of crustaceans listed in the Animal Health Law were identified based on evidence generated through an extensive literature review, to support a possible updating of Regulation (EU) 2018/1882. Crustacean species on or in which Taura syndrome virus (TSV), Yellow head virus (YHV) or White spot syndrome virus (WSSV) were identified, in the field or during experiments, were classified as reservoir species with different levels of certainty depending on the diagnostic tests used. Where experimental evidence indicated transmission of the pathogen from a studied species to another known susceptible species, the studied species was classified as vector species. Although the quantification of the risk of spread of the pathogens by the vectors or reservoir species was not part of the terms of reference, such risks do exist for the vector species, since transmission from infected vector species to susceptible species was proven. Where evidence for transmission from infected crustaceans was not found, these were defined as reservoirs. Nonetheless, the risk of the spread of the pathogens from infected reservoir species cannot be excluded. Evidence identifying conditions that may prevent transmission by vectors during transport was collected from scientific literature. It was concluded that it is very likely to almost certain (90-100%) that WSSV, TSV and YHV will remain infective at any possible transport condition. Therefore, vector or reservoir species that may have been exposed to these pathogens in an affected area in the wild or aquaculture establishments or by water supply can possibly transmit WSSV, TSV and YHV., (© 2023 European Food Safety Authority. EFSA Journal published by Wiley‐VCH GmbH on behalf of European Food Safety Authority.)

Vector or reservoir species of five mollusc diseases listed in the Animal Health Law were identified, based on evidence generated through an extensive literature review, to support a possible updating of Regulation (EU) 2018/1882. Mollusc species on or in which Mikrocytos mackini , Perkinsus marinus , Bonamia exitiosa , Bonamia ostreae and Marteilia refringens were detected, in the field or during experiments, were classified as reservoir species with different levels of certainty depending on the diagnostic tests used. Where experimental evidence indicated transmission of the pathogen from a studied species to another known susceptible species, this studied species was classified as a vector species. Although the quantification of the risk of spread of the pathogens by the vectors or reservoir species was not part of the terms of reference, such risks do exist for the vector species, since transmission from infected vector species to susceptible species was proven. Where evidence for transmission from infected molluscs was not found, these were defined as reservoir. Nonetheless, the risk of the spread of the pathogens from infected reservoir species cannot be excluded. Evidence identifying conditions that may prevent transmission by vectors or reservoir mollusc species during transport was collected from scientific literature. It was concluded that it is very likely to almost certain (90-100%) that M. mackini, P. marinus, B. exitiosa B. ostreae and M. refringens will remain infective at any possible transport condition. Therefore, vector or reservoir species that may have been exposed to these pathogens in an affected area in the wild or at aquaculture establishments or through contaminated water supply can possibly transmit these pathogens. For transmission of M. refringens, the presence of an intermediate host, a copepod, is necessary., (© 2023 European Food Safety Authority. EFSA Journal published by Wiley‐VCH GmbH on behalf of European Food Safety Authority.)

Vector or reservoir species of five fish diseases listed in the Animal Health Law were identified, based on evidence generated through an extensive literature review (ELR), to support a possible updating of Regulation (EU) 2018/1882. Fish species on or in which highly polymorphic region-deleted infectious salmon anaemia virus (HPR∆ ISAV), Koi herpes virus (KHV), epizootic haematopoietic necrosis virus (EHNV), infectious haematopoietic necrosis virus (IHNV) or viral haemorrhagic septicaemia virus (VHSV) were detected, in the field or during experiments, were classified as reservoir species with different levels of certainty depending on the diagnostic tests used. Where experimental evidence indicated transmission of the pathogen from a studied species to another known susceptible species, the studied species was classified as a vector species. Although the quantification of the risk of spread of the pathogens by the vectors or reservoir species was not part of the terms or reference, such risks do exist for the vector species, since transmission from infected vector species to susceptible species was proven. Where evidence for transmission from infected fish was not found, these were defined as reservoirs. Nonetheless, the risk of the spread of the pathogens from infected reservoir species cannot be excluded. Evidence identifying conditions that may prevent transmission by vectors or reservoir fish species during transport was collected from scientific literature. For VHSV, IHNV or HPR∆ ISAV, it was concluded that under transport conditions at temperatures below 25°C, it is likely (66-90%) they will remain infective. Therefore, vector or reservoir species that may have been exposed to these pathogens in an affected area in the wild, aquaculture establishments or through water supply can possibly transmit VHSV, IHNV or HPR∆ ISAV into a non-affected area when transported at a temperature below 25°C. The conclusion was the same for EHN and KHV; however, they are likely to remain infective under all transport temperatures., (© 2023 European Food Safety Authority. EFSA Journal published by Wiley‐VCH GmbH on behalf of European Food Safety Authority.)