Phytoplankton size- and abundance-based resilience assessments reveal nutrient rather than water level effects

Baho, Didier L.; Drakare, Stina; Johnson, Richard K.; Angeler, David G.

doi:10.1016/j.scitotenv.2020.141110

Cited by 5 publications

(2 citation statements)

References 66 publications

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“…(Adiguzel et al, 2020) Although every worker has his or her own dreams, at least as long as he or she farms with us, he or she knows very well what must be done to support the company to achieve profit, good reputation, and sustainability. Jumawan 562 (Baho et al, 2020) Human Resources play an important role in the success and development of a company or an organization. Every organization certainly hopes to achieve the vision and mission or goals that have been set.…”

Section: Theoretical Reviewmentioning

confidence: 99%

Succession Planning: Analysis Competence, Work Motivation, Strategic Leadership Development and Assessment Employee

Jumawan

2023

FJMR

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This study aims to find out what factors can support the success of succession planning in a corporate organization, this study uses a literature review of several articles related to dependent and independent, in this study does not use a questionnaire distribution involving respondents, the findings of this study are all variables independent variables significantly influence the dependent variable, so succession planning is successful if there is support or influence from the independent variables.

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Section: Theoretical Reviewmentioning

confidence: 99%

Succession Planning: Analysis Competence, Work Motivation, Strategic Leadership Development and Assessment Employee

Jumawan

2023

FJMR

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“…The size spectra seem to be quite variable in many studies of lentic macro-invertebrate communities (Hanson et al, 1989;Morin and Nadon, 1991). The size and abundance of phytoplankton showed variability with nutrients concentration rather than with depth in the lake ecosystem (Baho et al, 2020). Thus, this study was carried out to find the body mass distributions of macro-invertebrates in a range of static and slow moving irrigating water bodies and to analyze the relationship of size based indicator for ecological resilience of these irrigating water courses using rigorous approach such as Kernel Density Estimates (KDE) (Tsuyuk et al, 2012).…”

Section: Hamida Bibi 1 * Dave Rraffaelli 2 and Muhammad Sharifmentioning

confidence: 99%

The Variation of Size Distributions of Benthic Communities Across a Range of Irrigating Ponds and Canals of North Yorkshire, UK

Bibi¹,

Rraffaelli²,

Sharif³

2021

SJA

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Variation in the predictability of lake plankton metric types

Kakouei

Kraemer

Adrian

2022

Limnology & Oceanography

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Statistical and climate models are frequently used for biodiversity projections under future climatic changes, but their predictive capacity for freshwater plankton may vary among different species and community metrics. Here, we used random forests to model plankton species and community metrics as a function of biological, climatic, physical, and chemical data from long-term (2000-2017) monitoring data collected from Lake Müggelsee Berlin, Germany. We (1) compared the predictability of well-known lake plankton metric types (biomass, abundance, taxonomic diversity, Shannon diversity, Simpson diversity, evenness, taxonomic distinctness, and taxonomic richness) and ( 2) assessed how the relative influence of different environmental drivers varies across lake plankton metric models. Overall, the metric predictability was highest for biomass and abundance followed by taxonomic richness. The biomass of dominant phytoplankton taxonomic groups such as cyanobacteria (adjusted-R 2 = 0.53) and the abundance of dominant zooplankton taxonomic groups such as rotifers (adjusted-R 2 = 0.59) and daphnids (adjusted-R 2 = 0.51) were more predictable than other metric types. The plankton metric predictability increased when grouping phytoplankton species according to their functional traits (adjusted-R 2 = 0.37 AE 0.14, mean AE SD, n = 36 functional groups) compared to higher taxonomic units (adjusted-R 2 = 0.25 AE 0.15, n = 22 taxonomic groups). Light, nutrients, water temperature, and seasonality for phytoplankton and food resources for zooplankton were the main drivers of both taxonomic and functional groups, giving confidence that our models captured the expected major environmental drivers. Our quantitative analyses highlight the multidimensionality of lake planktonic responses to environmental drivers and have implications for our capacity to select appropriate metrics for forecasting the future of lake ecosystems under global change scenarios.

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Phytoplankton size- and abundance-based resilience assessments reveal nutrient rather than water level effects

Cited by 5 publications

References 66 publications

Succession Planning: Analysis Competence, Work Motivation, Strategic Leadership Development and Assessment Employee

Succession Planning: Analysis Competence, Work Motivation, Strategic Leadership Development and Assessment Employee

The Variation of Size Distributions of Benthic Communities Across a Range of Irrigating Ponds and Canals of North Yorkshire, UK

Variation in the predictability of lake plankton metric types

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