ABSTRACT
In recent decades, Saiga antelope (Saiga t. tatarica) mass die-offs have become more common. The mass die-off of 2015 in central Kazakhstan, recorded 140,000 individual deaths across multiple herds. Previously, research has shown atmospheric humidity, the bacterium Pasteurella multocida serotype B, and resultant haemorrhagic septicaemia, were the primary cause. However, other synergistic factors may have impacted this process. Here we use a multivariate compositional data analysis (CoDA) approach to assess what other factors may have been involved. We show a pollutant linkage mechanism where relative humidity and dewpoint temperature combine with environmental pollutants, potentially toxic elements (e.g., Hg, As), complex carbon compounds (e.g., Acetone, Toluene), and inorganic compounds (e.g., CHx, SO2) which affected the Saiga during the calving season (start and peak) and at the onset of the mass die-off. We suggest a mechanism for this process. Upon arrival at their carving grounds, the Saiga experienced a sudden precipitation event, a spike in temperatures, and resultant high humidity occurs. The infectious bacterium P. multocida serotype B then spreads. Further, environmental pollutants contained within steppe soils are released to the air, forming localised smog events, these synergistically combine, and mass die-off occurs.
Subject(s)
Antelopes , Environmental Pollutants , Animals , Antelopes/microbiology , Climate Change , KazakhstanABSTRACT
Potentially Toxic Elements (PTEs) otherwise known as heavy metals are ubiquitous in soils and can have a range of negative health and environmental impacts. In terrestrial systems understanding how PTEs move in the environment is made challenging by the complex interactions within soil and the wider environment and the compositional nature of PTEs. PTEs are compositional because data of individual PTEs within in a sample are ratios which may be under a sum constraint, where individual components sum up to a whole. In this study three different scenarios were considered, one using the centred log ratio transformation (clr) a compositional transformation, the more "traditional" log10 transformation (log10) and untransformed data acting as a comparison (unt) were applied to four different datasets. Three were the Liver, Muscle and Kidney tissue of Eurasian Badgers (Meles meles) and the fourth was soil and data were extracted from a regional geospatial survey. Cluster analysis demonstrated that the clr and log10 transformation were able to resolve compositional trends at the point of the individual sample, whilst unt could not and did not meet the preconditions for the next phase of analysis. At the level of compositional trends between PTEs complex heatmaps demonstrated that clr was able to isolate PTE relationships and highlight commonalities between different datasets, whilst log10 could not. In the final phase, principal component analysis (PCA) of the clr transformation showed similarities between the signals in the soft tissues and the disparities they had with soil, whilst the log10 transformation was unable to achieve this. Overall, the clr transformation was shown to perform more consistently under a variety of analytical scenarios and the compositional approach will provide more realistic interpretations about PTEs in both soil and animal soft tissue than the log10 or unt conditions.
Subject(s)
Metals, Heavy , Soil Pollutants , Animals , Ecotoxicology , Environmental Monitoring , Metals, Heavy/analysis , Metals, Heavy/toxicity , Soil , Soil Pollutants/analysis , Soil Pollutants/toxicityABSTRACT
Potentially Toxic Elements (PTEs) in Badgers (Meles meles), otherwise known as heavy metals, are unique amongst environmental pollutants occurring, both naturally and anthropogenically. PTEs have a broad range of negative health and environmental effects, therefore identifying their sources and pathways through the environment is imperative for public health policy. This is difficult in terrestrial systems due to the compositional nature of soil geochemistry. In this study, a compositional statistical approach was used to identify how PTEs accumulate in a terrestrial carnivorous mammal, Eurasian Badgers (Meles meles). Compositional principal component analysis (PCA) was used on geochemical data from the Tellus survey, the soil baseline and badger tissue data to map geo-spatial patterns of PTEs and show accumulative trends measured in time. Mapping PCs identified distinct regions of PTE presence in soil and PTE accumulation in badger tissues in Northern Ireland. PTEs were most elevated in liver, kidney and then muscle tissues. Liver and kidney showed the most distinct geo-spatial patterns of accumulation and muscle was the most depleted. PC1 and 2 for each type were modelled using generalised additive mixed models (GAMM) to identify trends through time. PC1 for the liver and muscle were associated with rainfall and ∂N15 in the liver, showing a link to diet and a bioaccumulation pathway, whilst PC2 for both tissues was associated with mean temperature, showing a link to seasonal activity and a bioaccessibility pathway. However, in kidney tissue these trends are reversed and PC1 was associated with bioaccessibility and PC2 with bioaccumulation. Combined these techniques can elucidate both geo-spatial trends in PTEs and the mechanisms by which they move in environment and in future may be an effective tool for assessing PTE bioavailability in environmental health surveys.