Linking the Community and Metacommunity Perspectives: Biotic Relationships Are Key in Benthic Diatom Ecology
Abstract
:1. Introduction
1.1. Community and Metacommunity of Benthic Diatoms: The Scale
1.2. Drivers of BD Distribution: Environmental Factors, Habitat and Substrate
1.3. Goals of This Study
2. Material and Methods
2.1. Field Work
2.2. Data Collection Matrices
2.3. Population–Community Structure and Its Control
2.4. Metacommunity Structure, Its Control and Spatial Scales
3. Results
3.1. Benthic Diatom Assemblages and Their Controlling Factors
3.2. Metacommunity Structure: Beta Diversity
3.3. The Metacommunity: Variance Partition and Spatial Patterns
4. Discussion
4.1. Benthic Diatom Populations–Communities and Their Control Factors
4.2. Metacommunity Structure: βD Patterns
4.3. The Metacommunity: Variance Partition and Spatial Patterns
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Site | Area Extent, km2; (Spacing, km); [Altitude, masl] | Substrate; (Water Trophic Status) | Dependent Variable (Number of Taxa) | Statistics Used | Concluding Remarks | Reference |
---|---|---|---|---|---|---|
White Creek (NY, USA) | 1.6 × 10−5 (4.9 × 10−5) | Cobble (-) | Taxa relative abundances (41) | Moran’s correlograms on dominant species; CCA on spatial features and current velocity and taxa | Patch length and width of BDA were >3.1 × 0.5–1 m; space explained much lower variability in diatom distribution than current velocity | [3] |
USA (whole country) | 8.1 × 106 (3.2) | Soft sediment and stone (whole range) | Taxa absolute abundances (433) | RDA on spatial and environmental factors and taxa | The environment plays the most important role in structuring stream BDA, but spatial factors also explain some variation in diatom distribution, especially at the more coarse scale (i.e., continental) | [4] |
Mesta river (Bulgaria) | 5.0 × 103 (6.5) | Cobble (whole range) | Taxa relative abundances | RDAs on environmental, temporal and spatial factors and taxa | All three independent matrices explain variability in BDA | [5] |
River Viaur (France) | 1.5 × 103 (3.0) [150–1090] | Cobble | Taxa relative abundances (196) | Mantel correlograms | BDA are spatially autocorrelated; man-made barriers are important for fragmentation of BDA | [6] |
Finland (whole country) | 3.4 × 105 (5.5) | Cobble (whole range) | Richness of taxa (248) | Nestedness and partial Mantel tests on environmental and spatial factors | Idiosyncratic species show faster turnover and are more widely distributed than nested species | [7] |
Guadiana basin (Spain) | 6.8 × 104 (1.1) [550–1000] | Cobble (whole range) | Taxa relative abundances (248) | CCAs on environmental, temporal and spatial factors and taxa | Environmental factors mostly structure BDA, but purely spatial control also takes place | [8] |
Two Finland catchments | (-) | Cobble (whole range) | Richness of taxa | MRM regressions of taxa nestedness on environmental and spatial factors | Nestedness mostly adheres to the local environment, but a minor variability can be attributed to the geographical longitude | [9] |
Dalälven catchment (Sweden) | 1.4 × 104 (3.9) [146–631] | Cobble | Taxa absolute abundances (186) | RDAs on PCNMs and taxa | Environmental factors mostly structure BDA | [10] |
122 stream sites at NE Spain | 3.2 × 104 (1.5) | Cobble (whole range) | Richness and nestedness | LM of taxa richness and ß diversity on local features | BDA inhabiting hydrologically stable rivers present a higher level of order in spatial pattern and a proportion of specialist taxa than communities in intermittent streams | [11] |
France (whole country) | 5.5 × 105 (0.5) [0–500] | Cobble (whole range) | Taxa relative abundances (1091) | MRM of taxa and environmental factors; Mantel correlograms | Environmental factors mostly structure BDA, but purely spatial control also takes place. Some ecoregions are neatly separated on account of geographical barriers | [12] |
USA (whole country) | 8.1 × 106 (6.1) | Sediment and water column (whole range) | Taxa relative abundances | RDAs of taxa on environmental and spatial data; co-inertial analysis | Water column and sediment assemblages are congruent and correlated regarding drivers of community composition | [13] |
Manyame catchment (Zimbabwe) | 4.4 × 104 (10.1) | Cobble (polluted water) | Taxa relative abundances (156) | CCA of hydromorphological factors and organic and heavy metal pollution and taxa | Hydromorphology and pollution partly explained the matrix of relative abundances of BD | [14] |
Canshang Erhai N. N. Reserve (China) | 9.5 × 102 (0.5) | Cobble (pristine water) | Taxa absolute abundances (149) | RDAs on PCNMs and taxa | Mountain barriers limit dispersal, which occurs through corridor streams | [15] |
Six data sets worldwide | [1400–4100] | Cobble | Taxa and T-type richness | NMDS and RDAs of Diatom taxa and T-types on environmental and spatial factors | Taxa composition discriminated the geographical regions better, while T-type composition detected the environmental gradients better | [16] |
Four southern Finland catchments | (-) | Cobble (eutrophic water) | Taxa relative abundances | ANOSIM, DCA and Mantel tests on environmental, spatial and temporal factors and taxa | Three-yearly temporal variation is negligible in the diatom–environment relationship | [17] |
Three northern Finland catchments | 6.4 × 104 (5.6) | Cobble (near-pristine water) | Taxa relative abundances | PCA to define metacommunities visually; RDAs on environmental and spatial factors and taxa; MEM on taxa; beta diversity assessment on taxa occurrence | Basin identity was a slightly better predictor of BDA than local environment; beta diversity of regions is high | [18] |
Southern half of Finland | (-) | Cobble (whole range) | Taxa absolute abundances | Richness, LCBD, SCBD; DBMEMs, RDAs, spatial autocorrelation of beta diversity; landscape features as independent variables | While richness and beta diversity of streams are related to the regional environment, those of lakes are related to spatial measurements; differential hydrological connectivity is the key factor of these diatom variables | [19] |
146 subarctic ponds (Finland and Norway) | [10–1080] | Cobble | Richness | LM and RDAs of richness and beta diversity on local features, a terrestrial vegetation index and elevation | Richness and beta diversity are mainly determined by local factors, loosely linked to elevation | [20] |
Finland Baltic coast | (-) | Cobble | Richness (230) | RDAs of taxa richness on environmental and spatial factors | Richness primarily regulated by local factors, while climatic and spatial variables have little impact on richness | [21] |
169 (for genus) and 52 (for species) USA lakes | (-) | Sediment | ß diversity (LCBD) before 1850 and in 2007 | LM of genus and species beta diversity on environmental and spatial factors | Beta diversity does not appear to have changed in the last 150 years; temporal beta diversity was related to land cover changes in watersheds | [22] |
France (whole country) | 5.5 × 105 (0.5) [0–500] | Cobble (whole range) | ß diversity on diatom presence | Partial Mantel tests on environment and beta diversity matrices | Environmental filtering is more important to beta diversity than space, which gains importance in middle and lower parts of catchments | [23] |
21 catchments in SW Finland | 1.7 × 105 (4.0) | Cobble (whole range) | Richness (347) | RDAs on environmental and spatial factors and taxa | Biogeographical variation of BDA results from the interplay of local, catchment and climatic variables, but also it is likely that dispersal limitation plays a role | [24] |
USA and Finland (whole countries) | 8.1 × 106 (5.4) [0–2448] and 3.4 × 105 (5.8) [0–302] | Cobble (whole range) | Richness and distribution of absolute abundance | LM of taxa richness and SAD on climatic and chemical features | The spatial patterns of richness and abundance defined primarily by the covariance of climate and chemistry with space | [25] |
38 Carpathian lakes (Hungary) | [73–311] | Reed, stone, mud | ß diversity (LCBD, SCBD) and relative abundance | LM and RDAs of dependent variables on spatial and environmental heterogeneity | Spatial and environmental variables affect diatom features | [26] |
34 lakes in whole of Europe | (-) | Reed (whole range) | Richness | RDAs of beta diversity on environmental and spatial factors | Taxa richness is mainly due to environmental factors | [27] |
26 streams in the Orinoco catchment (Colombia) | 4.0 × 104 (7.7) [300–3400] | Cobble and other rocks (whole range) | Taxa and trait relative abundances (297) | ANOSIM of BDA in ecoregions; RDAs on environmental, spatial and historical factors and taxa and traits | Constraints on taxa occurrence and dispersal, as well as legacies of historical events, explain contemporary distribution of diatoms in the area | [28] |
Lake Bolshoe Toko (Yakutia, Russia) | 8.3 × 101 (0.5) [903] | (-) | DNA and morphological taxa relative abundances | Estimation of alpha and beta diversity (LCBD and SCBD); RDAs on vertical space and morphotaxa and DNA taxa | Genetic diversity was higher than morphodiversity; alpha and beta diversity responded differently to lake depth | [29] |
Location Name | Lat. (Decim.) | Long. (Decim.) | Habitat |
---|---|---|---|
Júcar Catchment | |||
Algarra river at Algarra | 40.000389 | 1.440572 | F |
Cabriel river at Alcalá de la Vega | 40.031719 | 1.514378 | F |
Cabriel river at Boniches | 39.983250 | 1.641311 | F |
Cabriel river at Salvacañete | 40.097005 | 1.508172 | F |
Cabriel river at Villar del Humo | 39.840572 | 1.663689 | F |
Cabriel springs | 40.235075 | 1.554969 | S |
Guadarroyo river upstream Valdemoro Sierra | 40.104069 | 1.754736 | F |
Júcar river at Huélamo | 40.279128 | 1.814058 | F |
Júcar river at Uña | 40.221797 | 1.978241 | F |
Júcar river close to its spring | 40.364077 | 1.829153 | F |
La Toba reservoir | 40.211447 | 1.922105 | S |
Laguna river downstream Laguna del Marquesado | 40.169611 | 1.672333 | F |
Marquesado lake | 40.187522 | 1.666727 | S |
Mayor river downstream Cañete | 40.010319 | 1.657928 | F |
Mayor river upstream Cañete | 40.053000 | 1.631094 | F |
Tejadillos river at Cañete | 40.066125 | 1.623122 | F |
Uña lake | 40.224167 | 1.977777 | S |
Valdemoro Sierra spring and cascade | 40.078117 | 1.776656 | F |
Vencherque river at Villar del Humo | 40.053000 | 1.631094 | F |
Tajo Catchment | |||
Alcantud river downstream Alcantud | 40.512253 | 2.329152 | F |
Beteta wetland | 40.566338 | 2.072000 | S |
Cuervo river at Solán de Cabras | 40.512906 | 2.127116 | F |
Cuervo river at the spring | 40.428511 | 1.889386 | F |
Cuervo river at Vega del Codorno | 40.422828 | 1.913333 | F |
Cuervo river upstream Santa María | 40.499953 | 2.035769 | F |
El Tobar lake | 40.545714 | 2.048944 | S |
El Tobar lake spring | 40.546547 | 2.044671 | S |
Escabas river at Tejadillos | 40.394575 | 1.983691 | F |
Escabas river downstream Cañamares | 40.447552 | 2.247922 | F |
Escabas river downstream Fuertescusa | 40.468480 | 2.222775 | F |
Escabas river upstream Guadiela junction | 40.447552 | 2.247922 | F |
Guadiela river at Beteta | 40.576058 | 2.043937 | F |
Guadiela river upstream La Ruidera reservoir | 40.509641 | 2.323303 | F |
Guadiela river upstream Puente Vadillos | 40.532544 | 2.149589 | F |
La Ruidera reservoir | 40.478502 | 2.376797 | S |
La Tosca reservoir | 40.517600 | 2.058514 | S |
Masegar creek at El Tobar | 40.551572 | 2.063969 | F |
Molino de Chincha reservoir | 40.538200 | 2.161030 | S |
Overall | Catchments | Habitats | Substrates | ||||
---|---|---|---|---|---|---|---|
Júcar | Tajo | Stagnant w. | Streams | Epilithic | Epiphytic | ||
Number of samples | 132 | 69 | 63 | 24 | 108 | 44 | 88 |
α diversity indices | |||||||
Richness range | 3–29 | 3–22 | 5–29 | 6–29 | 3–26 | 6–27 | 3–29 |
Average | 12.7 | 10.8 | 14.8 | 15.5 | 12.1 | 12.5 | 12.9 |
SD | 5.0 | 4.1 | 5.2 | 5.7 | 4.7 | 5.6 | 4.8 |
p | 0.0001 | 0.0004 | 0.4283 | ||||
Shannon index range | 0.11–2.77 | 0.12–2.28 | 0.14–2.77 | 0.64–2.32 | 0.11–2.77 | 0.64–2.77 | 0.11–2.28 |
Effective number range | 1.12–15.89 | 1.12–9.78 | 1.15–15.89 | 1.91–10.13 | 1.12–15.89 | 1.89–15.89 | 1.12–9.78 |
Average | 4.23 | 3.98 | 4.51 | 4.17 | 4.25 | 4.59 | 4.06 |
SD | 2.21 | 1.85 | 2.52 | 2.17 | 2.22 | 2.72 | 1.89 |
p | 0.172 | 0.961 | 0.193 | ||||
Main taxa | |||||||
Achnanthidium minutissimum | 76 (73%) | 76 (63%) | 70 (84%) | 76 (79%) | 70 (71%) | 69 (64%) | 76 (77%) |
Cocconeis placentula | 91 (67%) | 91 (76%) | 63 (59%) | 19 (58%) | 91 (69%) | 80 (64%) | 91 (69%) |
Cymbella affinis | 64 (60%) | 42 (54%) | 64 (67%) | 64 (50%) | 42 (63%) | 64 (55%) | 38 (63%) |
Cymbella delicatula | 64 (34%) | 64 (24%) | 54 (46%) | 29 (38%) | 64 (33%) | 42 (34%) | 64 (34%) |
Cymbopleura amphicephala | 61 (53%) | 38 (38%) | 61 (84%) | 61 (79%) | 0 (0%) | 46 (55%) | 61 (63%) |
Diatoma vulgaris | 98 (46%) | 98 (44%) | 22 (49%) | 4 (29%) | 98 (50%) | 22 (36%) | 98 (51%) |
Gomphonema angustatum | 50 (77%) | 49 (75%) | 50 (81%) | 46 (75%) | 50 (78%) | 49 (77%) | 50 (77%) |
Navicula cryptotenella | 55 (42%) | 55 (47%) | 32 (37%) | 1 (21%) | 55 (46%) | 39 (36%) | 55 (44%) |
β diversity indices | |||||||
Harrison index | 0.09 | 0.13 | 0.12 | 0.2 | 0.09 | 0.18 | 0.11 |
NODF (nestedness) | 26.3 | 23.9 | 26.1 | 29.7 | 27.5 | 22.7 | 25.6 |
Spatial turnover | 0.96 | 0.94 | 0.9 | 0.73 * | 0.95 | 0.9 | 0.93 |
Dissimilarity due to nestedness | 0.02 | 0.03 | 0.05 | 0.11 * | 0.02 | 0.04 | 0.03 |
Dependent Variable | Independent Variables | Slope | p | Adj R2 | N |
---|---|---|---|---|---|
Taxa In Stagnant Waters | |||||
Cymbella minuta | Nitrate | 0.247 | 0.040 | 0.25 | 14 |
Achnanthidium minutissimum | Epithemia goeppertiana | −1.101 | 0.008 | 0.31 | 24 |
Rhopalodia gibba | −1.079 | 0.025 | |||
Epithemia goeppertiana | 1.221 | 0.000 | |||
Cymbella affinis | Cymbella minuta | −1.203 | 0.000 | 0.77 | 24 |
Achnanthidium minutissimum | −0.146 | 0.034 | |||
Epithemia goeppertiana | 0.857 | 0.000 | |||
Cymbella minuta | Cymbella affinis | −0.587 | 0.000 | 0.8 | 24 |
Achnanthidium minutissimum | −0.109 | 0.023 | |||
Epithemia goeppertiana | Cymbella minuta | 0.807 | 0.000 | 0.82 | 24 |
Cymbella affinis | 0.567 | 0.000 | |||
TAXA IN STREAMS | |||||
Achnanthidium minutissimum | pH | −0.364 | 0.000 | 0.17 | 73 |
Cocconeis placentula | pH | 0.388 | 0.001 | 0.28 | 73 |
Water temperature | −0.036 | 0.000 | |||
Cymbella affinis | Water temperature | 0.008 | 0.006 | 0.16 | 73 |
Nitrite | −1.575 | 0.016 | |||
Cymbella cesatii | % Oxygen | 0.004 | 0.000 | 0.51 | 73 |
Cymbella helvetica | Water temperature | 0.003 | 0.019 | 0.11 | 73 |
Stream velocity | −0.060 | 0.018 | |||
Cymbopleura amphicephala | Water temperature | 0.008 | 0.011 | 0.15 | 73 |
DIN | −0.060 | 0.011 | |||
Fragilaria dilatata | Ammonia | −0.386 | 0.002 | 0.23 | 73 |
Melosira varians | Ammonia | 2.172 | 0.000 | 0.25 | 73 |
Navicula cryptotenella | pH | 0.247 | 0.000 | 0.25 | 73 |
Substrate type | 0.011 | 0.016 | |||
Fish herbivory | −0.083 | 0.002 | |||
POC | −0.219 | 0.015 | |||
Achnanthidiium minutissimum | Melosira varians | −0.385 | 0.000 | 0.40 | 108 |
Diatoma vulgaris | −0.446 | 0.000 | |||
Gomphonema angustatum | −0.586 | 0.000 | |||
Gyrosigma attenuatum | −0.586 | 0.005 | |||
Cymbella cesatii | −0.448 | 0.002 | |||
Ulnaria ulna | −1.121 | 0.005 | |||
Cymbella helvetica | −1.401 | 0.007 | |||
Cocconeis placentula | Achnanthidium minutissimum | −0.577 | 0.000 | 0.44 | 108 |
Melosira varians | −0.514 | 0.000 | |||
Cymbella cesatii | −0.562 | 0.001 | |||
Diatoma vulgaris | −0.469 | 0.002 | |||
Gyrosigma attenuatum | −0.699 | 0.005 | |||
Fragilaria dilatata | −1.149 | 0.006 | |||
Cymbella delicatula | −0.580 | 0.005 | |||
Navicula cryptotenella | −0.531 | 0.008 | |||
Cymbella affinis | Gomphonema angustatum | 0.211 | 0.002 | 0.13 | 108 |
Fragilaria delicatissima | Ulnaria ulna | 0.627 | 0.000 | 0.32 | 108 |
Fragilaria dilatata | Gomphonema angustatum | 0.145 | 0.000 | 0.14 | 108 |
Cymbella helvetica | 0.334 | 0.013 | |||
Gomphonema angustatum | Fragilaria dilatata | 0.801 | 0.000 | 0.2 | 108 |
Ellerbeckia arenaria | 0.471 | 0.029 | |||
Cymbella affinis | 0.343 | 0.004 | |||
Ulnaria ulna | Fragilaria delicatissima | 0.545 | 0.000 | 0.37 | 108 |
Cymbella affinis | 0.080 | 0.039 | |||
Achnanthidium minutissimum | −0.037 | 0.018 |
TAXA | Overall | Júcar Catchment | Tajo Catchment | Stagnant Water Bodies | Streams | Epilithic | Epiphytic |
---|---|---|---|---|---|---|---|
Number of Samples | 132 | 69 | 63 | 24 | 108 | 44 | 88 |
Two Independent Matrices | |||||||
Spatialized Environment | 47 | 49 | 37 | 33 | 48 | 45 | 51 |
Pure Space | 3 | 2 | 0 | 0 | 3 | 0 | 3 |
Pure Environment | 6 | 6 | 6 | 1 | 9 | 6 | 7 |
Unexplained | 51 | 48 | 62 | 68 | 49 | 55 | 49 |
Four Independent Matrices | |||||||
Spatialized Physico-chemical | 42 | 44 | 31 | 34 | 40 | 39 | 43 |
Spatialized Catchment | 39 | 45 | 31 | 34 | 39 | 35 | 40 |
Spatialized Biological | 16 | 25 | 18 | 22 | 11 | 14 | 18 |
Pure Biological | 6 | 9 | 7 | 8 | 4 | 14 | 6 |
Unexplained | 52 | 46 | 62 | 58 | 54 | 47 | 50 |
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Álvarez-Cobelas, M.; Rojo, C. Linking the Community and Metacommunity Perspectives: Biotic Relationships Are Key in Benthic Diatom Ecology. Water 2022, 14, 3805. https://doi.org/10.3390/w14233805
Álvarez-Cobelas M, Rojo C. Linking the Community and Metacommunity Perspectives: Biotic Relationships Are Key in Benthic Diatom Ecology. Water. 2022; 14(23):3805. https://doi.org/10.3390/w14233805
Chicago/Turabian StyleÁlvarez-Cobelas, Miguel, and Carmen Rojo. 2022. "Linking the Community and Metacommunity Perspectives: Biotic Relationships Are Key in Benthic Diatom Ecology" Water 14, no. 23: 3805. https://doi.org/10.3390/w14233805
APA StyleÁlvarez-Cobelas, M., & Rojo, C. (2022). Linking the Community and Metacommunity Perspectives: Biotic Relationships Are Key in Benthic Diatom Ecology. Water, 14(23), 3805. https://doi.org/10.3390/w14233805