1.

Getis Ord Gi* hotspots of species richness, Shannon-Wiener diversity, and Simpson’s diversity within the study segment in the San Saba River, TX.

2.

Getis Ord Gi* hotspots of species richness, Shannon-Wiener diversity, and Simpson’s diversity within the study segment in the San Saba River, TX.

3.

Getis Ord Gi* hotspots of species richness, Shannon-Wiener diversity, and Simpson’s diversity within the study segment in the San Saba River, TX.

4.

Getis Ord Gi* hotspots of species richness, Shannon-Wiener diversity, and Simpson’s diversity within the study segment in the San Saba River, TX.

5.

Getis Ord Gi* hotspots of species richness, Shannon-Wiener diversity, and Simpson’s diversity within the study segment in the San Saba River, TX.

6.

Getis Ord Gi* hotspots of species richness, Shannon-Wiener diversity, and Simpson’s diversity within the study segment in the San Saba River, TX.

7.

Getis Ord Gi* hotspots of species richness, Shannon-Wiener diversity, and Simpson’s diversity within the study segment in the San Saba River, TX.

8.

Getis Ord Gi* hotspots of species richness, Shannon-Wiener diversity, and Simpson’s diversity within the study segment in the San Saba River, TX.

9.

Getis Ord Gi* hotspots of species richness, Shannon-Wiener diversity, and Simpson’s diversity within the study segment in the San Saba River, TX.

1.

Results of Wilcoxon tests comparing average hydraulic conditions during low (0.42 m 3 s -1 ), moderate (5.32 m 3 s -1 ), moderate-high, (32.28 m 3 s -1 ), and high (361.89 m 3 s -1...

2.

Results of Wilcoxon tests comparing average hydraulic conditions during low (0.42 m 3 s -1 ), moderate (5.32 m 3 s -1 ), moderate-high, (32.28 m 3 s -1 ), and high (361.89 m 3 s -1...

3.

Results of Wilcoxon tests comparing average hydraulic conditions during low (0.42 m 3 s -1 ), moderate (5.32 m 3 s -1 ), moderate-high, (32.28 m 3 s -1 ), and high (361.89 m 3 s -1...

4.

Results of Wilcoxon tests comparing average hydraulic conditions during low (0.42 m 3 s -1 ), moderate (5.32 m 3 s -1 ), moderate-high, (32.28 m 3 s -1 ), and high (361.89 m 3 s -1...

5.

Results of Wilcoxon tests comparing average hydraulic conditions during low (0.42 m 3 s -1 ), moderate (5.32 m 3 s -1 ), moderate-high, (32.28 m 3 s -1 ), and high (361.89 m 3 s -1...

6.

Results of Wilcoxon tests comparing average hydraulic conditions during low (0.42 m 3 s -1 ), moderate (5.32 m 3 s -1 ), moderate-high, (32.28 m 3 s -1 ), and high (361.89 m 3 s -1...

7.

Results of Wilcoxon tests comparing average hydraulic conditions during low (0.42 m 3 s -1 ), moderate (5.32 m 3 s -1 ), moderate-high, (32.28 m 3 s -1 ), and high (361.89 m 3 s -1...

8.

Results of Wilcoxon tests comparing average hydraulic conditions during low (0.42 m 3 s -1 ), moderate (5.32 m 3 s -1 ), moderate-high, (32.28 m 3 s -1 ), and high (361.89 m 3 s -1...

9.

Results of Wilcoxon tests comparing average hydraulic conditions during low (0.42 m 3 s -1 ), moderate (5.32 m 3 s -1 ), moderate-high, (32.28 m 3 s -1 ), and high (361.89 m 3 s -1...

1.

Variable importance plots for random forest classification examining the response of freshwater mussel presence to modeled hydraulic variables during a low (0.42 m3s-1), moderate (...

2.

Variable importance plots for random forest classification examining the response of freshwater mussel presence to modeled hydraulic variables during a low (0.42 m3s-1), moderate (...

3.

Variable importance plots for random forest classification examining the response of freshwater mussel presence to modeled hydraulic variables during a low (0.42 m3s-1), moderate (...

4.

Variable importance plots for random forest classification examining the response of freshwater mussel presence to modeled hydraulic variables during a low (0.42 m3s-1), moderate (...

5.

Variable importance plots for random forest classification examining the response of freshwater mussel presence to modeled hydraulic variables during a low (0.42 m3s-1), moderate (...

6.

Variable importance plots for random forest classification examining the response of freshwater mussel presence to modeled hydraulic variables during a low (0.42 m3s-1), moderate (...

7.

Variable importance plots for random forest classification examining the response of freshwater mussel presence to modeled hydraulic variables during a low (0.42 m3s-1), moderate (...

8.

Variable importance plots for random forest classification examining the response of freshwater mussel presence to modeled hydraulic variables during a low (0.42 m3s-1), moderate (...

9.

Variable importance plots for random forest classification examining the response of freshwater mussel presence to modeled hydraulic variables during a low (0.42 m3s-1), moderate (...

1.

Studies examining the impacts of hydraulic conditions on freshwater mussels at different flows in variable environments at multiple spatial scales.

2.

Studies examining the impacts of hydraulic conditions on freshwater mussels at different flows in variable environments at multiple spatial scales.

3.

Studies examining the impacts of hydraulic conditions on freshwater mussels at different flows in variable environments at multiple spatial scales.

4.

Studies examining the impacts of hydraulic conditions on freshwater mussels at different flows in variable environments at multiple spatial scales.

5.

Studies examining the impacts of hydraulic conditions on freshwater mussels at different flows in variable environments at multiple spatial scales.

6.

Studies examining the impacts of hydraulic conditions on freshwater mussels at different flows in variable environments at multiple spatial scales.

7.

Studies examining the impacts of hydraulic conditions on freshwater mussels at different flows in variable environments at multiple spatial scales.

8.

Studies examining the impacts of hydraulic conditions on freshwater mussels at different flows in variable environments at multiple spatial scales.

9.

Studies examining the impacts of hydraulic conditions on freshwater mussels at different flows in variable environments at multiple spatial scales.

1.

Abundance and presence of species found at 200 sampling sites partitioned amongst 70 mesohabitat units in the San Saba River, TX during 2018 surveys.

2.

Abundance and presence of species found at 200 sampling sites partitioned amongst 70 mesohabitat units in the San Saba River, TX during 2018 surveys.

3.

Abundance and presence of species found at 200 sampling sites partitioned amongst 70 mesohabitat units in the San Saba River, TX during 2018 surveys.

4.

Abundance and presence of species found at 200 sampling sites partitioned amongst 70 mesohabitat units in the San Saba River, TX during 2018 surveys.

5.

Abundance and presence of species found at 200 sampling sites partitioned amongst 70 mesohabitat units in the San Saba River, TX during 2018 surveys.

6.

Abundance and presence of species found at 200 sampling sites partitioned amongst 70 mesohabitat units in the San Saba River, TX during 2018 surveys.

7.

Abundance and presence of species found at 200 sampling sites partitioned amongst 70 mesohabitat units in the San Saba River, TX during 2018 surveys.

8.

Abundance and presence of species found at 200 sampling sites partitioned amongst 70 mesohabitat units in the San Saba River, TX during 2018 surveys.

9.

Abundance and presence of species found at 200 sampling sites partitioned amongst 70 mesohabitat units in the San Saba River, TX during 2018 surveys.

1.

Flows simulated using HEC-RAS, including exceedance probabilities for USGS gage 08144500 (San Saba at Menard) for both the period of record (1916–2022) and 1998–2018 and date and m...

2.

Flows simulated using HEC-RAS, including exceedance probabilities for USGS gage 08144500 (San Saba at Menard) for both the period of record (1916–2022) and 1998–2018 and date and m...

3.

Flows simulated using HEC-RAS, including exceedance probabilities for USGS gage 08144500 (San Saba at Menard) for both the period of record (1916–2022) and 1998–2018 and date and m...

4.

Flows simulated using HEC-RAS, including exceedance probabilities for USGS gage 08144500 (San Saba at Menard) for both the period of record (1916–2022) and 1998–2018 and date and m...

5.

Flows simulated using HEC-RAS, including exceedance probabilities for USGS gage 08144500 (San Saba at Menard) for both the period of record (1916–2022) and 1998–2018 and date and m...

6.

Flows simulated using HEC-RAS, including exceedance probabilities for USGS gage 08144500 (San Saba at Menard) for both the period of record (1916–2022) and 1998–2018 and date and m...

7.

Flows simulated using HEC-RAS, including exceedance probabilities for USGS gage 08144500 (San Saba at Menard) for both the period of record (1916–2022) and 1998–2018 and date and m...

8.

Flows simulated using HEC-RAS, including exceedance probabilities for USGS gage 08144500 (San Saba at Menard) for both the period of record (1916–2022) and 1998–2018 and date and m...

9.

Flows simulated using HEC-RAS, including exceedance probabilities for USGS gage 08144500 (San Saba at Menard) for both the period of record (1916–2022) and 1998–2018 and date and m...

1.

Complex hydraulic variables calculated in the U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System (HEC-RAS).

2.

Complex hydraulic variables calculated in the U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System (HEC-RAS).

3.

Complex hydraulic variables calculated in the U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System (HEC-RAS).

4.

Complex hydraulic variables calculated in the U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System (HEC-RAS).

5.

Complex hydraulic variables calculated in the U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System (HEC-RAS).

6.

Complex hydraulic variables calculated in the U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System (HEC-RAS).

7.

Complex hydraulic variables calculated in the U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System (HEC-RAS).

8.

Complex hydraulic variables calculated in the U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System (HEC-RAS).

9.

Complex hydraulic variables calculated in the U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System (HEC-RAS).

1.

Results of random forest (RF) classification models examining the response of freshwater mussel presence to hydraulic conditions at simulated flows using site-level and mesohabitat...

2.

Results of random forest (RF) classification models examining the response of freshwater mussel presence to hydraulic conditions at simulated flows using site-level and mesohabitat...

3.

Results of random forest (RF) classification models examining the response of freshwater mussel presence to hydraulic conditions at simulated flows using site-level and mesohabitat...

4.

Results of random forest (RF) classification models examining the response of freshwater mussel presence to hydraulic conditions at simulated flows using site-level and mesohabitat...

5.

Results of random forest (RF) classification models examining the response of freshwater mussel presence to hydraulic conditions at simulated flows using site-level and mesohabitat...

6.

Results of random forest (RF) classification models examining the response of freshwater mussel presence to hydraulic conditions at simulated flows using site-level and mesohabitat...

7.

Results of random forest (RF) classification models examining the response of freshwater mussel presence to hydraulic conditions at simulated flows using site-level and mesohabitat...

8.

Results of random forest (RF) classification models examining the response of freshwater mussel presence to hydraulic conditions at simulated flows using site-level and mesohabitat...

9.

Results of random forest (RF) classification models examining the response of freshwater mussel presence to hydraulic conditions at simulated flows using site-level and mesohabitat...

1.

Results of random forest (RF) regression models examining the response of freshwater mussel log(x+1) species’ abundances to hydraulic conditions at simulated flows.

2.

Results of random forest (RF) regression models examining the response of freshwater mussel log(x+1) species’ abundances to hydraulic conditions at simulated flows.

3.

Results of random forest (RF) regression models examining the response of freshwater mussel log(x+1) species’ abundances to hydraulic conditions at simulated flows.

4.

Results of random forest (RF) regression models examining the response of freshwater mussel log(x+1) species’ abundances to hydraulic conditions at simulated flows.

5.

Results of random forest (RF) regression models examining the response of freshwater mussel log(x+1) species’ abundances to hydraulic conditions at simulated flows.

6.

Results of random forest (RF) regression models examining the response of freshwater mussel log(x+1) species’ abundances to hydraulic conditions at simulated flows.

7.

Results of random forest (RF) regression models examining the response of freshwater mussel log(x+1) species’ abundances to hydraulic conditions at simulated flows.

8.

Results of random forest (RF) regression models examining the response of freshwater mussel log(x+1) species’ abundances to hydraulic conditions at simulated flows.

9.

Results of random forest (RF) regression models examining the response of freshwater mussel log(x+1) species’ abundances to hydraulic conditions at simulated flows.

1.

Adsorbate-Dependent Electronic Structure Descriptors for Machine Learning-Driven Binding Energy Predictions in Diverse Single Atom Alloys: A Reductionist Approach

2.

Adsorbate-Dependent Electronic Structure Descriptors for Machine Learning-Driven Binding Energy Predictions in Diverse Single Atom Alloys: A Reductionist Approach

3.

Adsorbate-Dependent Electronic Structure Descriptors for Machine Learning-Driven Binding Energy Predictions in Diverse Single Atom Alloys: A Reductionist Approach

4.

Adsorbate-Dependent Electronic Structure Descriptors for Machine Learning-Driven Binding Energy Predictions in Diverse Single Atom Alloys: A Reductionist Approach

5.

Adsorbate-Dependent Electronic Structure Descriptors for Machine Learning-Driven Binding Energy Predictions in Diverse Single Atom Alloys: A Reductionist Approach

6.

Adsorbate-Dependent Electronic Structure Descriptors for Machine Learning-Driven Binding Energy Predictions in Diverse Single Atom Alloys: A Reductionist Approach

7.

Adsorbate-Dependent Electronic Structure Descriptors for Machine Learning-Driven Binding Energy Predictions in Diverse Single Atom Alloys: A Reductionist Approach

8.

Adsorbate-Dependent Electronic Structure Descriptors for Machine Learning-Driven Binding Energy Predictions in Diverse Single Atom Alloys: A Reductionist Approach

9.

Adsorbate-Dependent Electronic Structure Descriptors for Machine Learning-Driven Binding Energy Predictions in Diverse Single Atom Alloys: A Reductionist Approach