Influence of Selected Environmental Factors on Diatom β Diversity (Bacillariophyta) and the Value of Diatom Indices and Sampling Issues
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Chemical and Physical Data
2.3. Diatom Data
2.4. Statistical Data
2.4.1. β Diversity of Diatoms
2.4.2. Seasonal Differences, Between-Lake Differences, and Effectiveness of Indices
2.5. Spatial Diversity
3. Results
3.1. β Diversity
3.2. Seasonal Differences, Differences between Lakes, and Effectiveness of Indices
3.3. Spatial Diversity
4. Discussion
4.1. β Diversity
4.2. Spatial and Seasonal Differences
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Name of Lake | Lake Area (ha) | Max. Depth (m) | Coastline Length (m) | Direct Catchment (ha) | Catchment (ha) |
---|---|---|---|---|---|
1. Okrągłe (O) | 13.7 | 13 | 1459 | 28.5 | 906.8 |
2. Białe Wigierskie (BW) | 99.9 | 34 | 5117 | 329.1 | 329.1 |
3. Krusznik (K) | 26.7 | 18 | 2643 | 70.7 | 70.7 |
4. Wigry (W) | 2163.3 | 73 | 63,920 | 5159.8 | 45,293.1 |
Lakes | Cl− | CO32− | SO42− | NO3− | NH4+ | Mg2+ | PO43− | Ca2+ | pH | Conductivity |
---|---|---|---|---|---|---|---|---|---|---|
(mg/L) | (µS/cm) | |||||||||
O | 4.10–9.96 | 103.52–144.61 | 12.21–28.56 | 0.03–0.22 | 0.036–0.23 | 9.17–16.17 | 0.000–0.003 | 34.85–73.46 | 7.31–8.26 | 231–361 |
BW | 2.70–2.78 | 73.39–94.93 | 5.92–6.34 | 0.00–0.02 | 0.01–0.13 | 5.53–6.96 | 0.000–0.005 | 25.05–38.07 | 7.28–8.44 | 161–174 |
K | 4.46–10.21 | 127.44–129.29 | 12.33–27.23 | 0.22–0.24 | 0.036–0.23 | 8.63–13.85 | 0.002–0.001 | 35.87–57.07 | 7.29–8.33 | 251–327 |
WK | 16.31–17.06 | 129.32–147.83 | 21.68–23.47 | 0.01–0.09 | 0.012–0.12 | 13.09–16.07 | 0.000–0.005 | 42.05–67.50 | 7.55–8.25 | 349–374 |
WS | 15.82–16.50 | 119.93–155.12 | 20.61–22.39 | 0.01–0.05 | 0.01–0.12 | 12.96–17.14 | 0.000–0.001 | 40.93–67.94 | 7.26–8.41 | 339–378 |
WP | 16.18–17.02 | 130.66–141.17 | 20.56–22.31 | 0.01–0.03 | 0.20–1.26 | 13.16–16.67 | 0.001–0.001 | 39.77–61.53 | 7.65–8.26 | 334–370 |
WB | 15.31–17.02 | 129.92–150.38 | 20.64–22.36 | 0.02–0.07 | 0.00–0.20 | 13.11–16.78 | 0.001–0.006 | 42.51–67.38 | 7.51–8.21 | 352–372 |
Analysis | LCBD Total | LCBD Turnover | LCBD Nestedness | Beta Sørensen Total | Beta Sørensen Turnover | Beta Sørensen Nestedness | |
---|---|---|---|---|---|---|---|
Function | |||||||
1. Redundancy analysis | Model variance constrained | 0.3763 | 0.4172 | 0.2580 | 1.056 | 0.3421 | 0.716 |
Model variance uncontrained | 4.6237 | 4.5828 | 4.742 | 3.944 | 4.6579 | 4.284 | |
Model p | 0.011 * | 0.006 ** | 0.048 * | 0.001 ** | 0.017 * | 0.001 ** | |
Axis p | 0.008 ** | 0.006 ** | 0.048 * | 0.002 ** | 0.012 * | 0.002 ** | |
2. Variation partitioning | Adj R2SO42− | 0.013 | 0.01842 | −0.02015 | 0.16979 | −0.02171 | 0.33897 |
Adj R2NO3− | 0.00721 | −0.01534 | 0.09121 | 0.45749 | 0.13798 | 0.20454 | |
Adj R2PO43− | 0.04678 | 0.03172 | 0.01942 | 0.00153 | −0.02019 | 0.00510 | |
Adj R2NH4+ | −0.01761 | −0.01066 | −0.01324 | 0.04875 | 0.03053 | −0.01343 | |
3. Multiple regression on distance matrices | p SO42− | - | - | - | - | - | - |
p NO3− | 0.001 ** | 0.001 ** | - | 0.004 ** | 0.020 * | - | |
p Ca2+ | - | - | - | 0.001 ** | 0.001 ** | ||
p Cl− | 0.001 ** | - | |||||
p PO43− | 0.032 * | 0.018 * | - | - | - | - | |
R2 | 0.1085 | 0.1124 | Not significant | 0.2041 | 0.05953 | 0.1297 |
Analysis | LCBD Total | LCBD Turnover | LCBD Nestedness | Beta Sørensen Total | Beta Sørensen Turnover | Beta Sørensen Nestedness | |
---|---|---|---|---|---|---|---|
1. Random forest—best parameters | Bootstrap | True | True | True | True | True | True |
Max depth | 80 | 110 | 50 | 30 | 80 | 80 | |
Max features | 3 | Sqrt | 3 | 2 | 2 | 2 | |
Min samples leaf | 3 | 2 | 2 | 2 | 1 | 1 | |
Min samples split | 5 | 2 | 2 | 4 | 4 | 4 | |
N estimators | 600 | 400 | 200 | 100 | 200 | 200 | |
OOB score | True | True | True | True | True | True | |
2. Random forest feature importance (rfpimp) | SO42− | 0.305 | 0.232 | 0.128 | 0.419 | 0.241 | 0.140 |
NO3− | 0.266 | 0.201 | 0.122 | 0.229 | 0.236 | 0.293 | |
Ca2+ | 0.130 | 0.146 | 0.562 | 0.403 | 0.176 | 0.400 | |
Cl− | 0.210 | 0.278 | 0.121 | 0.079 | 0.212 | 0.150 | |
NH4+ | 0.067 | 0.097 | 0.189 | 0.195 | 0.125 | 0.167 | |
PO43− | 0.083 | 0.106 | 0.083 | 0.062 | 0.104 | 0.098 | |
R2 | 0.83 | 0.85 | 0.89 | 0.88 | 0.82 | 0.81 | |
3. Random forest feature importance (eli5) | SO42− | 0.299 | 0.241 | 0.129 | 0.409 | 0.251 | 0.123 |
NO3− | 0.236 | 0.193 | 0.135 | 0.245 | 0.263 | 0.284 | |
Ca2+ | 0.132 | 0.159 | 0.536 | 0.351 | 0.169 | 0.371 | |
Cl− | 0.210 | 0.320 | 0.147 | 0.0756 | 0.223 | 0.166 | |
NH4+ | 0.071 | 0.104 | 0.191 | 0.160 | 0.143 | 0.167 | |
PO43− | 0.098 | 0.148 | 0.096 | 0.055 | 0.100 | 0.91 | |
OOB score | 0.52 | 0.42 | 0.55 | 0.44 | 0.31 | 0.28 | |
4. Linear model (single values) | p SO42− | 0.0020 ** | 0.000811 *** | - | 0.0311 * | - | 0.00000005 *** |
p NO3− | 0.000549 *** | 0.002330 ** | 0.0204 * | 0.00000126 *** | 0.00541 ** | 0.00000126 *** | |
p Ca2+ | 0.000284 *** | 0.000240 *** | - | - | - | 0.0000852 *** | |
p Cl− | - | - | - | - | - | - | |
p PO43− | - | - | - | - | - | - | |
p NH4+ | 0.0399 * | 0.044987 * | - | - | - | 0.0123 * | |
Adj R2 | 0.26 | 0.257 | 0.0921 | 0.4992 | 0.138 | 0.597 |
Lakes | Taxon 1 | Indicator Value | p | Taxon 2 | Indicator Value | p | Taxon 3 | Indicator Value | p |
---|---|---|---|---|---|---|---|---|---|
| Nitzschia palea | 0.31 | 0.025 | - | - | - | - | - | - |
| Gomphonema procerum | 0.52 | 0.001 | Encyonema ventricosum | 0.46 | 0.002 | Nitzschia lacuum | 0.39 | 0.001 |
| Brachysira neoexilis | 0.45 | 0.001 | Brachysira procera | 0.44 | 0.03 | Eunotia arcubus | 0.43 | 0.004 |
| Cymbella excisa | 0.65 | 0.001 | Fragilaria subconstricta | 0.51 | 0.002 | Cocconeis placentula | 0.41 | 0.006 |
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Eliasz-Kowalska, M.; Wojtal, A.Z.; Barinova, S. Influence of Selected Environmental Factors on Diatom β Diversity (Bacillariophyta) and the Value of Diatom Indices and Sampling Issues. Water 2022, 14, 2315. https://doi.org/10.3390/w14152315
Eliasz-Kowalska M, Wojtal AZ, Barinova S. Influence of Selected Environmental Factors on Diatom β Diversity (Bacillariophyta) and the Value of Diatom Indices and Sampling Issues. Water. 2022; 14(15):2315. https://doi.org/10.3390/w14152315
Chicago/Turabian StyleEliasz-Kowalska, Monika, Agata Z. Wojtal, and Sophia Barinova. 2022. "Influence of Selected Environmental Factors on Diatom β Diversity (Bacillariophyta) and the Value of Diatom Indices and Sampling Issues" Water 14, no. 15: 2315. https://doi.org/10.3390/w14152315
APA StyleEliasz-Kowalska, M., Wojtal, A. Z., & Barinova, S. (2022). Influence of Selected Environmental Factors on Diatom β Diversity (Bacillariophyta) and the Value of Diatom Indices and Sampling Issues. Water, 14(15), 2315. https://doi.org/10.3390/w14152315