3. 2008, 2010 – Montana: 2010 – two bison confirmed on test kit and lab diagnostics for Ba – 12 cows on Zapata County ranch – confirmed in cows and soil IN A POTHOLE!
2009 – major epizootic near Ozona. One case, suspected in 2010; 4 cases on RV ranch – confirmed
2010 – major epizootic. Southern most points of the west Texas box – Louis deer and Tully goats; eastern most – confirmed Cottle’s place’ western two sites – RV and Jones; northern most in Texas dots – 2009/10 Walker.

4. Recently confirmed we have vulture feathers and vomited castes positive for Ba. We also confirmed the water trough was positive for at least 65 days and the carcasses for over 1 year. I also confirmed a bison at 2+ years in Montana this summer.

5. Top left – Us in Texas in Oct 2009; Top Right – our cart in west Texas 2010; bottom left – my fly traps in Montana Aug 2010; bottom right – the RV Ranch 2005

6. SAMPLING A POSITIVE DEER IN EASTERN MOST PORTION OF WEST TEXAS IN JULY 2010.

7. Positive deer ear, positive maggots, positive adult fly – MLVA-25 confirms all same strain. Leaves were contaminated too, and confirmed on PCR – with no quantity for typing. So – deer, maggots, fly, leaves all confirmed in one sample for west Texas, July 2010.

8. Defining local clusters: Getis’ G statistic
Can we detect the spectral signature of potholes? Do those values cluster on the landscape?
Finally, the 2009 Zapata County shows that we have soil persistence for at least 3 months. This slide illustrates that potholes cluster and we can detect that and confirm that with remote sensing statistically.
30 meter bandwidth
Conservative cutoff 3.8 (p<0.0001) - ~7000 grid cells
Fullbright – dc = 30 m, rather than 1 or 60 m; part of a cluster here as i + 1 neighbor;
A combo of a short dc and blue band (moisture) is a reasonable tool for delineating boundaries because they fail to significant at large distances, or clusters clearly end along what we define from field measurements as transition zones. Moisture is a steep gradient and transition out of a pothole is quite dramatic (see the soils map). This is partially (likely) due to relatively quick changes in elevation from inside to outside of the pothole.
SD average above other areas moisture, so clearly delineating areas of HIGH moisture.
Year round signal, today showing some prelim stuff.
February 14, 1999 (WINTER)
November 13, 1999
Blackburn et al. (2009 Bacillus ACT)

9. The graphic illustrates what we know about the population of deer (now extending it through 2010) and known outbreaks or cases. Original graph from Blackburn (2006) now extended through 2010.

10. Predicted distribution of Bacillus anthracis
Blackburn et al. (2007); Blackburn (2010)

11. Bangladesh model on its own
Bangladesh model on its own. Using the green dots to build and yellow to test with:
Precip total
Driest month
Wettest month
Mean temp
Temp range
Soil pH
Soil moisture
Elevation
Data sets in the model

12. US prediction projected on the Bangladesh using the Joyner et al
US prediction projected on the Bangladesh using the Joyner et al. (2010) variables. The US model does not capture the Bangladesh points – or there is no signal in the US model for Bangladesh points. The outbreaks in Bangladesh are outside of the US anthrax model.

13. Geographic distributions and climate change
WNA Lineage
1960 thru 1990
Figure 8. A- the potential geographic distribution of Bacillus anthracis in the contiguous United States based on a nine-variable ecological niche modeling experiment based on current climatic conditions from the HADCM3 B2 future climate scenario. B – the potential geographic distribution of B. anthracis in 2050 using the future climate data from the HADCM3 B2 climate scenario. A threshold of six models or better was selected to visualize these analyses.
Ames
We can evaluate/speculate on climate change and the impacts of climate on epidemiology and geographic distributions. We can also think about evolutionary implications of climate changes on pathogen diversity. Speculating on the SNP results of Simonson et al. (2009), we are predicting a loss of Ames environments, not WNA.
2050
Blackburn (2010)

14. Geographic distributions and climate change
A2 climate change scenario
B2 climate change scenario
A3b
A1a
Difference in A2 and B2
We can evaluate/speculate on climate change and the impacts of climate on epidemiology and geographic distributions (Blackburn 2010, Joyner et al. 2010). We can also think about evolutionary implications of climate changes on pathogen diversity. Speculating on the MLVA-8 results of Aikimbayev et al. (2010), we are predicting a loss of A1a diversity and possible A3.
2 current and difference; 2 future and difference
Joyner et al. (2010)