Matches in SemOpenAlex for { <https://semopenalex.org/work/W2891366790> ?p ?o ?g. }
- W2891366790 endingPage "9750" @default.
- W2891366790 startingPage "9739" @default.
- W2891366790 abstract "Ellenberg indicator values (EIVs) are a widely used metric in plant ecology comprising a semi-quantitative description of species' ecological requirements. Typically, point estimates of mean EIV scores are compared over space or time to infer differences in the environmental conditions structuring plant communities-particularly in resurvey studies where no historical environmental data are available. However, the use of point estimates as a basis for inference does not take into account variance among species EIVs within sampled plots and gives equal weighting to means calculated from plots with differing numbers of species. Traditional methods are also vulnerable to inaccurate estimates where only incomplete species lists are available.We present a set of multilevel (hierarchical) models-fitted with and without group-level predictors (e.g., habitat type)-to improve precision and accuracy of plot mean EIV scores and to provide more reliable inference on changing environmental conditions over spatial and temporal gradients in resurvey studies. We compare multilevel model performance to GLMMs fitted to point estimates of mean EIVs. We also test the reliability of this method to improve inferences with incomplete species lists in some or all sample plots. Hierarchical modeling led to more accurate and precise estimates of plot-level differences in mean EIV scores between time-periods, particularly for datasets with incomplete records of species occurrence. Furthermore, hierarchical models revealed directional environmental change within ecological habitat types, which less precise estimates from GLMMs of raw mean EIVs were inadequate to detect. The ability to compute separate residual variance and adjusted R2 parameters for plot mean EIVs and temporal differences in plot mean EIVs in multilevel models also allowed us to uncover a prominent role of hydrological differences as a driver of community compositional change in our case study, which traditional use of EIVs would fail to reveal. Assessing environmental change underlying ecological communities is a vital issue in the face of accelerating anthropogenic change. We have demonstrated that multilevel modeling of EIVs allows for a nuanced estimation of such from plant assemblage data changes at local scales and beyond, leading to a better understanding of temporal dynamics of ecosystems. Further, the ability of these methods to perform well with missing data should increase the total set of historical data which can be used to this end." @default.
- W2891366790 created "2018-09-27" @default.
- W2891366790 creator A5010128458 @default.
- W2891366790 creator A5031502178 @default.
- W2891366790 creator A5041764112 @default.
- W2891366790 creator A5068399505 @default.
- W2891366790 creator A5089430818 @default.
- W2891366790 date "2018-09-06" @default.
- W2891366790 modified "2023-10-18" @default.
- W2891366790 title "Improving estimates of environmental change using multilevel regression models of Ellenberg indicator values" @default.
- W2891366790 cites W1583274184 @default.
- W2891366790 cites W1625136202 @default.
- W2891366790 cites W1643313396 @default.
- W2891366790 cites W1965478509 @default.
- W2891366790 cites W1967985831 @default.
- W2891366790 cites W1970582009 @default.
- W2891366790 cites W1972941851 @default.
- W2891366790 cites W1977258292 @default.
- W2891366790 cites W1981457167 @default.
- W2891366790 cites W2000170423 @default.
- W2891366790 cites W2017152718 @default.
- W2891366790 cites W2018469333 @default.
- W2891366790 cites W2024535413 @default.
- W2891366790 cites W2048089239 @default.
- W2891366790 cites W2055424972 @default.
- W2891366790 cites W2057347111 @default.
- W2891366790 cites W2071017459 @default.
- W2891366790 cites W2084253034 @default.
- W2891366790 cites W2092846892 @default.
- W2891366790 cites W2096304256 @default.
- W2891366790 cites W2105090664 @default.
- W2891366790 cites W2124380570 @default.
- W2891366790 cites W2126121631 @default.
- W2891366790 cites W2129023025 @default.
- W2891366790 cites W2129163149 @default.
- W2891366790 cites W2156207071 @default.
- W2891366790 cites W2157701121 @default.
- W2891366790 cites W2157902608 @default.
- W2891366790 cites W2159267296 @default.
- W2891366790 cites W2171010898 @default.
- W2891366790 cites W2312433770 @default.
- W2891366790 cites W2476077119 @default.
- W2891366790 cites W2525840300 @default.
- W2891366790 cites W2529607432 @default.
- W2891366790 cites W2571395702 @default.
- W2891366790 cites W2891366790 @default.
- W2891366790 cites W4230096730 @default.
- W2891366790 cites W55036437 @default.
- W2891366790 doi "https://doi.org/10.1002/ece3.4422" @default.
- W2891366790 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6202714" @default.
- W2891366790 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30386571" @default.
- W2891366790 hasPublicationYear "2018" @default.
- W2891366790 type Work @default.
- W2891366790 sameAs 2891366790 @default.
- W2891366790 citedByCount "10" @default.
- W2891366790 countsByYear W28913667902020 @default.
- W2891366790 countsByYear W28913667902022 @default.
- W2891366790 countsByYear W28913667902023 @default.
- W2891366790 crossrefType "journal-article" @default.
- W2891366790 hasAuthorship W2891366790A5010128458 @default.
- W2891366790 hasAuthorship W2891366790A5031502178 @default.
- W2891366790 hasAuthorship W2891366790A5041764112 @default.
- W2891366790 hasAuthorship W2891366790A5068399505 @default.
- W2891366790 hasAuthorship W2891366790A5089430818 @default.
- W2891366790 hasBestOaLocation W28913667901 @default.
- W2891366790 hasConcept C105795698 @default.
- W2891366790 hasConcept C11413529 @default.
- W2891366790 hasConcept C121955636 @default.
- W2891366790 hasConcept C126838900 @default.
- W2891366790 hasConcept C144133560 @default.
- W2891366790 hasConcept C14898019 @default.
- W2891366790 hasConcept C149782125 @default.
- W2891366790 hasConcept C150921843 @default.
- W2891366790 hasConcept C154945302 @default.
- W2891366790 hasConcept C155512373 @default.
- W2891366790 hasConcept C162324750 @default.
- W2891366790 hasConcept C167651023 @default.
- W2891366790 hasConcept C176217482 @default.
- W2891366790 hasConcept C183115368 @default.
- W2891366790 hasConcept C18903297 @default.
- W2891366790 hasConcept C196083921 @default.
- W2891366790 hasConcept C21547014 @default.
- W2891366790 hasConcept C2776214188 @default.
- W2891366790 hasConcept C33923547 @default.
- W2891366790 hasConcept C41008148 @default.
- W2891366790 hasConcept C53059260 @default.
- W2891366790 hasConcept C71924100 @default.
- W2891366790 hasConcept C83546350 @default.
- W2891366790 hasConcept C86803240 @default.
- W2891366790 hasConceptScore W2891366790C105795698 @default.
- W2891366790 hasConceptScore W2891366790C11413529 @default.
- W2891366790 hasConceptScore W2891366790C121955636 @default.
- W2891366790 hasConceptScore W2891366790C126838900 @default.
- W2891366790 hasConceptScore W2891366790C144133560 @default.
- W2891366790 hasConceptScore W2891366790C14898019 @default.
- W2891366790 hasConceptScore W2891366790C149782125 @default.
- W2891366790 hasConceptScore W2891366790C150921843 @default.
- W2891366790 hasConceptScore W2891366790C154945302 @default.