Research ArticleENVIRONMENTAL STUDIES

Saigas on the brink: Multidisciplinary analysis of the factors influencing mass mortality events

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Science Advances  17 Jan 2018:
Vol. 4, no. 1, eaao2314
DOI: 10.1126/sciadv.aao2314
  • Fig. 1 2015 MME sites showing dates of onset and the location of the two sites studied in detail in the field.

    Inset shows the location of the three saiga populations within Kazakhstan (x and y units are longitude and latitude, respectively).

  • Fig. 2 Photomicrographs showing multifocal, acute degenerative tissue changes with the light blue staining indicating Gram-negative bacteria consistent with the pure isolation of P. multocida.

    (A) Liver: necrosuppurative hepatitis, random with intralesional bacteria (arrow). (B) Spleen: splenitis, necrotizing with bacterial emboli. (C) Lung, perivascular hemorrhage (arrow) and edema. (D) Lymph node: lymphadenitis, necrosuppurative.

  • Fig. 3 Kernel density map of the first two components of a PCA of selected climatic variables in the 10 days to disease onset (or 9 May for controls for which exact date is not available), showing separation of die-off sites from other sites.

    See fig. S5 for indications of the relative contribution of each climate variable to these two components.

  • Fig. 4 Probability of die-off related to selected environmental variables in the 10 day period to onset, 1979 to 2015.

    (A) Fitted values for the probability of a die-off event against mean maximum daily relative humidity in the previous 10 days, with the three die-off years in different colors. (B) Fitted values for the probability of a die-off event against mean minimum daily temperature in the previous 10 days, with the three MME years in different colors.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/1/eaao2314/DC1

    Supplementary Text

    fig. S1. Locations of the three P. multocida–associated disease events (1981, 1988, and 2015) in the Betpak-dala population and control sites at which calving proceeded without a die-off since 1979, used in the climate analysis (x and y units are longitude and latitude).

    fig. S2. Photographs depicting the die-off of saiga and their clinical signs and pathology.

    fig. S3. Fluorescence in situ hydridization (FISH) photomicrographs showing position of fluorescing P. multocida bacteria.

    fig. S4. Box plots for cases (die-off sites) and controls (no die-off) using the full data set, with real dates of onset if available or 9 May if not, showing the relationship between cases/controls and selected climate metrics aggregated over 10-day periods before onset.

    fig. S5. Contributions of variables to components 1 and 2 of the PCA using the full data set, with real dates of onset if available or 9 May if not.

    table S1. PCR primers used for microorganism detection.

    table S2. A summary of the largest MMEs in saiga attributed to Pasteurellaceae-related syndromes.

    table S3. Results of an ICP-MS analysis of livers (w/w) of three dead saigas from May 2015 (Tengiz group).

    table S4. Assuming a diagnosis of HS as a primary cause of death, this table is a list of possible stressors, which may cause suppressed immunity in the host or increased virulence and invasion of a commensal parasite such as P. multocida, subsequent septicemia in infected hosts, and/or enhanced transmission.

    table S5. Comparison of means for cases and controls using the full data set (all MME years); real dates of onset or 9 May; and climate metrics aggregated over the 10 days to onset.

    table S6. Long-term climate anomaly data at Kostanai sites (ERA data using actual die-off site, NCEP data using pixel, covering entire Torgai area).

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Text
    • fig. S1. Locations of the three P. multocida–associated disease events (1981, 1988, and 2015) in the Betpak-dala population and control sites at which calving proceeded without a die-off since 1979, used in the climate analysis (x and y units are longitude and latitude).
    • fig. S2. Photographs depicting the die-off of saiga and their clinical signs and pathology.
    • fig. S3. Fluorescence in situ hydridization (FISH) photomicrograph showing position of fluorescing P. multocida bacteria.
    • fig. S4. Box plots for cases (die-off sites) and controls (no die-off) using the full data set, with real dates of onset if available or 9 May if not, showing the relationship between cases/controls and selected climate metrics aggregated over 10-day periods before onset.
    • fig. S5. Contributions of variables to components 1 and 2 of the PCA using the full data set, with real dates of onset if available or 9 May if not.
    • table S1. PCR primers used for microorganism detection.
    • table S2. A summary of the largest MMEs in saiga attributed to Pasteurellaceae-related syndromes.
    • table S3. Results of an ICP-MS analysis of livers (w/w) of three dead saigas from May 2015 (Tengiz group).
    • table S4. Assuming a diagnosis of HS as a primary cause of death, this table is a list of possible stressors, which may cause suppressed immunity in the host or
      increased virulence and invasion of a commensal parasite such as P. multocida, subsequent septicemia in infected hosts, and/or enhanced transmission.
    • table S5. Comparison of means for cases and controls using the full data set (all MME years); real dates of onset or 9 May; and climate metrics aggregated over the 10 days to onset.
    • table S6. Long-term climate anomaly data at Kostanai sites (ERA data using actual die-off site, NCEP data using pixel, covering entire Torgai area).

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