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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1007/s10646-023-02685-0
|2 doi
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|a pubmed25n1199.xml
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|a (DE-627)NLM359918522
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|a (NLM)37491685
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Loerracher, Ann-Kathrin
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Characterization of patterns and variability in the dynamics of outdoor aquatic mesocosms
|b exploring the capabilities and challenges in data supporting aquatic system models
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|c 2023
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 25.08.2023
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|a Date Revised 28.08.2023
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2023. The Author(s).
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|a Aquatic mesocosms are complex test systems used within regulatory risk assessment of plant protection products. These model ecosystems allow researchers to capture interactions of multiple species under realistic environmental conditions. They enable assessment of direct and indirect effects of stressors at all trophic levels (i.e., from primary producers to secondary consumers) and impacts on ecosystem functions. Due to the limited ability to test the multitude of potential exposure scenarios, cross-linking aquatic mesocosm studies with virtual mesocosms, i.e., aquatic system models (ASMs), can serve to meet the demand for more environmental realism and ecological relevance in risk assessment. In this study, full control data sets from seven aquatic mesocosm studies conducted at a single test facility under GLP were analysed graphically and using descriptive statistics. Thereby, not only a comprehensive data base but also an insight into the species present, their dynamics over time, and variability in unchallenged mesocosms was observed. While consistency in dynamics could be discerned for physical and chemical parameters, variability was evident for several biological endpoints. This variability points to amplification of small differences over time as well as to stochastic processes. The outline of existing gaps and uncertainties in data leads to the estimation of what can be expected to be captured and predicted by ASMs
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4 |
|a Journal Article
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| 650 |
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4 |
|a Aquatic mesocosm
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| 650 |
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4 |
|a Ecosystem
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| 650 |
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4 |
|a Historical control data
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| 650 |
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4 |
|a Mechanistic effect models
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| 650 |
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4 |
|a Model parametrization
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| 650 |
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4 |
|a Risk assessment
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| 650 |
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7 |
|a Water Pollutants, Chemical
|2 NLM
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| 700 |
1 |
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|a Schmidt, Jürgen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ebke, Peter
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Schmolke, Amelie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Abi-Akar, Farah
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Galic, Nika
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ashauer, Roman
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Ecotoxicology (London, England)
|d 1992
|g 32(2023), 6 vom: 06. Aug., Seite 782-801
|w (DE-627)NLM098212214
|x 1573-3017
|7 nnas
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| 773 |
1 |
8 |
|g volume:32
|g year:2023
|g number:6
|g day:06
|g month:08
|g pages:782-801
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| 856 |
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|u http://dx.doi.org/10.1007/s10646-023-02685-0
|3 Volltext
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|a AR
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|d 32
|j 2023
|e 6
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|c 08
|h 782-801
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