Temporal variation of diatom assemblages in oligotrophic and eutrophic streams

Korhonen, Jenni J.; Köngäs, Petrina; Soininen, Janne

doi:10.1080/09670262.2013.779390

Cited by 19 publications

(14 citation statements)

References 24 publications

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“…Furthermore, Korhonen et al . () have demonstrated that although diatom assemblages vary in time, compositional differences between assemblages growing in different types of environments, for example oligotrophic versus eutrophic waters, remain significant.…”

Section: Methodsmentioning

confidence: 99%

“…Although sampling occasions cover a wide temporal range, we consider these samples to be comparable because the sampling methods were identical. Furthermore, Korhonen et al (2013) have demonstrated that although diatom assemblages vary in time, compositional differences between assemblages growing in different types of environments, for example oligotrophic versus eutrophic waters, remain significant.…”

Section: Data Collectionmentioning

confidence: 99%

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Climate is an important driver for stream diatom distributions

Pajunen

Luoto

Soininen

2015

Global Ecology and Biogeography

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Aim The species distributions of macroorganisms are widely studied, yet microbial distributions at species level remain poorly resolved. We explored the relative contributions of climatic and local environmental factors in explaining the distribution patterns of unicellular diatoms. Location A geographical gradient of c. 1200 km in Finland (60°–70° N). Methods We modelled the distributions of 157 diatom taxa sampled in 227 stream sites using climatic and local environmental predictors with four different modelling techniques: generalized linear models, generalized additive models, boosted regression trees and Random Forest. We used models with three separate sets of predictors: environment only, climate only and the full set of predictors. The model performances were evaluated using the area under the curve of a receiver operating characteristic plot and the true skill statistic values by a four‐fold cross‐validation approach. Results We found that the predictive performance of the full models was highest, indicating the importance of both the local environment and large‐scale climatic factors in diatom distribution patterns. However, climate‐only models outcompeted the environment‐only models in predicting diatom distributions. The explanatory variables had varying importance across species and growing degree days and precipitation had the highest relative importance in the full models. We also found that the predictability of the distributions varied greatly among species, but the differences among families were typically small. Main conclusions Our results suggest that at a broad geographical scale climate‐related factors are important determinants of diatom distributions and may be stronger drivers than local variables. The inclusion of both climatic and local environmental factors in species distribution modelling facilitates the understanding of the joint effects of these drivers on microorganisms in future conditions. From an applied perspective, our study demonstrated that species distribution models serve as an important tool in explaining and predicting microbial distributions.

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Section: Methodsmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Climate is an important driver for stream diatom distributions

Pajunen

Luoto

Soininen

2015

Global Ecology and Biogeography

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“…We consider all samples to be comparable as they were sampled using the same methods. In addition, Korhonen, Köngäs & Soininen () demonstrated that diatom assemblages are robust indicators of water chemistry variables even though assemblages vary in time.…”

Section: Methodsmentioning

confidence: 99%

Stream diatom assemblages as predictors of climate

2016

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1. Benthic diatoms have been widely used as indicators of water quality in streams. New insights that diatoms may also respond to large-scale drivers, such as climate or historical factors, highlight the need to reassess the usefulness and the reliability of diatoms as bioindicators. 2. Using a suite of modelling techniques, weighted averaging (WA), weighted averaging partial least squares, modern-analogue technique (MAT) and two machine learning techniques, boosted regression trees (BRT) and random forests (RF), we calibrated models to infer water quality and climatic variables using diatom abundance data collected from 227 stream sites in Finland. 3. Predictive ability was generally better for climatic variables [growing degree days (GDD) defined as temperature >5°C, summer precipitation and water balance] than for local environmental variables (conductivity, water colour and total phosphorus). The strongest relationships were found for GDD (r 2 = 0.86, MAT) and conductivity (r 2 = 0.82, RF). Using BRT, we also identified potential indicator species for local environmental and climatic variables, based on relative importance species in the models. 4. Our results show that diatoms could serve as efficient proxies for climatic variables and local environmental conditions. Furthermore, new modelling techniques such as modern regression trees can provide new insights into relationships between diatom assemblages and local water quality and climate, and thus help to construct more reliable indices. These methods could also serve as important tools to infer environmental variables in changing ecosystems.

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“…Considering intra-annual and inter-annual dynamics, algal biomass and biodiversity display distinct seasonal cycles, and community composition changes among seasons and years (Miller et al, 2012;Tan et al, 2014). Long-term fluctuation tendencies of algal assemblages are synchronous with temporal nutrient dynamics and hydrological and climatic regimes (Reid et al, 2006;Korhonen et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Temporal patterns of community composition (represented by taxa abundance data) or temporal turnover (represented by similarity/dissimilarity matrices of community compositions across different sampling times) is also frequently addressed by treating algal community as a single metric (Passy, 2006;Korhonen et al, 2010Korhonen et al, , 2013. However, algal assemblages are composed of species possessing diverse biological characteristics and distinct environmental optima (Stevenson, 2014).…”

Section: Introductionmentioning

confidence: 99%

Multi-scale temporal dynamics of epilithic algal assemblages: evidence from a Chinese subtropical mountain river network

et al. 2015

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Research on multi-scale temporal dynamics of lotic algal assemblages remains scarce. In this study, we analyzed epilithic algae sampled monthly from a Chinese subtropical mountain river network from 2004 to 2007, by using a multivariate time series modeling approach. We hypothesized that (1) multiscale temporal dynamics exist within algal communities; (2) physical and chemical conditions drive algal temporal dynamics; and (3) tributary sites differ in algal temporal changes. This study revealed 2-4 sitespecific algal temporal dynamics, contributed by 23-45% component taxa. Among the time-related taxa, percentages of high profile guild taxa were higher than both the low profile and the motile guild taxa. Several algal temporal dynamics were found to be driven by water temperature, conductivity, or current velocity, within which influences of conductivity at two sites resulted in directional changes in algal communities. Furthermore, tributary sites differed in algal temporal changes when compared to the two mainstream sites. Our findings imply that natural fluctuations and agricultural disturbance together shaped algal temporal dynamics in the studied river network. In conclusion, for accurately tracking algal temporal dynamics, we recommend that long-term and high-frequency biomonitoring protocols are developed. Moreover, both the mainstream and tributary sites should be monitored simultaneously.

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Temporal variation of diatom assemblages in oligotrophic and eutrophic streams

Cited by 19 publications

References 24 publications

Climate is an important driver for stream diatom distributions

Climate is an important driver for stream diatom distributions

Stream diatom assemblages as predictors of climate

Multi-scale temporal dynamics of epilithic algal assemblages: evidence from a Chinese subtropical mountain river network

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