A bacterial index to estimate lake trophic level: National scale validation

Pearman, John K.; Wood, Susanna A.; Vandergoes, Marcus J.; Atalah, Javier; Waters, Sean; Adamson, Janet; Thomson‐Laing, Georgia; Thompson, L. M.; Howarth, Jamie; Hamilton, David P.; Pochon, Xavier; Biessy, Laura; Brasell, Katie A.; Dahl, Jenny; Ellison, Riki; Fitzsimons, Sean J.; Gard, Henry; Gerrard, Tania; Gregersen, Rose; Holloway, McKayla; Li, Xun; Kelly, David; Martin, Reece; MCFARLANE, K. B.; McKay, Nicholas P.; Moody, Adelaine; Moy, Christopher M.; Naeher, Sebastian; Newnham, Rewi M.; Parai, Russleigh; Picard, Maïlys; Puddick, Jonathan; Rees, Andrew; Reyes, Lizette; Schallenberg, Marc; Shepherd, Claire L.; Short, Julia; Simon, Kevin S.; Steiner, Konstanze; Sunde, Charlotte; Terezow, Marianna; Tibby, John

doi:10.1016/j.scitotenv.2021.152385

Cited by 12 publications

(13 citation statements)

References 79 publications

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“…dbRDA based on Bray–Curtis dissimilarity matrix on the sediment assemblage at the genus level showing environmental, spatial and abundant taxa relationships for bacteria ( A – C ), and eukaryotes ( D – F ). Points are colored by predicted trophic state calculated based on the bacteria assemblage based on Pearman et al (2022 b ). Alt, altitude; Area, lake area; Dep, maximum lake depth; HPG, high‐productivity grassland in catchment; Lat, latitude; LPG, low‐productivity grassland in catchment; Nat, native vegetation in catchment; S.D., Secchi disk depth; W.Chla, water column Chl a , W.DOC, water column dissolved organic carbon.…”

Section: Resultsmentioning

confidence: 99%

“…This index is based on the proportion of bacterial trophic state indicator ASVs in the lake. The indicator ASVs were determined based on the investigation of the bacterial assemblage in 96 monitored lakes which had a trophic lake index assigned to them (Pearman et al 2022b). The SBTI was calculated by the following equation:…”

Section: Discussionmentioning

confidence: 99%

“…Raw sequence reads are deposited in the NCBI short read archive under the accession number: PRJNA606991, PRJNA750120, and PRJNA813318. A small geographically restricted subset of the raw data was previously analyzed in Pearman et al (2020), while the bacterial ASV data from the majority of the lakes were used for the development of a sediment bacterial trophic index (Pearman et al 2022 b ).…”

Section: Methodsmentioning

confidence: 99%

“…A total of 13 environmental variables were retained for analysis (Supporting Information Table S2). As appropriate water quality data were not available for the majority of lakes, trophic state was determined for each lake based on the surface sediment bacterial trophic index (SBTI) as detailed in Pearman et al (2022 b ). This index is based on the proportion of bacterial trophic state indicator ASVs in the lake.…”

Section: Methodsmentioning

confidence: 99%

“…This index is based on the proportion of bacterial trophic state indicator ASVs in the lake. The indicator ASVs were determined based on the investigation of the bacterial assemblage in 96 monitored lakes which had a trophic lake index assigned to them (Pearman et al 2022 b ). The SBTI was calculated by the following equation:

SBTI = 0.017 \times % microtrophic + 0.026 \times % oligotrophic + 0.031 \times % mesotrophic + 0.040 \times % eutrophic + 0.055 \times % supertrophic + 0.067 \times % hypertrophic,

where, for example, %microtrophic is the % of indicator taxa in the sample that are microtrophic indicators.…”

Section: Methodsmentioning

confidence: 99%

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Deterministic processes drive national‐scale patterns in lake surface sediment bacteria and eukaryotic assemblage composition

Pearman

Adamson

Thomson‐Laing

et al. 2022

Limnology & Oceanography

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Biological communities within lake surface sediments play a vital role in biogeochemical cycling and ecosystem services. Knowledge on abundance‐occupancy patterns and assembly processes across large spatial scales and over multiple environmental gradients is limited, yet essential to aid in protection and restoration. In the present study, surface sediment samples were collected from 296 lakes across a wide spatial scale and covering multiple interacting environmental gradients including size, depth, altitude, and trophic state. A suite of physicochemical parameters were used to characterize the environmental conditions and bacterial and eukaryotic assemblages were determined using 16S and 18S rRNA metabarcoding. The majority (~ 55%) of amplicon sequence variants were only found in a single lake with eukaryotes having a more restricted distribution than bacteria. Deterministic processes were inferred to be dominant for both bacteria (78%) and eukaryotes (51%), with variable selection being especially important for bacteria (49%). Variation partitioning indicated that land use in the catchment, which is strongly related to trophic state, was the most important environmental factor in explaining the assemblage composition. This nationwide study across broad gradients provides new insights into the ecology of bacteria and eukaryotes in lake surface sediments and a platform to better understand the effects of multiple environmental stressors on lake sediment assemblages.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…This index is based on the proportion of bacterial trophic state indicator ASVs in the lake. The indicator ASVs were determined based on the investigation of the bacterial assemblage in 96 monitored lakes which had a trophic lake index assigned to them (Pearman et al 2022 b ). The SBTI was calculated by the following equation: