Biological Diversity: Frontiers in Measurement and Assessment

By Anne E. Magurran, Brian J. McGill. 2011

1,211 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ab2e0ad144c8ec14a348f93a53366eb304f93609

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Citations in the corpus

Unifying Species Diversity, Phylogenetic Diversity, Functional Diversity, and Related Similarity and Differentiation Measures Through Hill Numbers (2014, methodology): “See Magurran & McGill 2011, pp. 39–54 Graham CH, Fine PVA.”
Diversity and abundance of solitary and primitively eusocial bees in an urban centre: a case study from Northampton (England) (2015, background): “High frequency surveys throughout the season are particularly important to record rare species and species that are active over a short period (Magurran and McGill 2011; Banaszak et al. 2014).”
Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies (2014, unknown): “The measurement and assessment of biological diversity (biodiversity) is an active research focus of ecology (Magurran 2004, Magurran and McGill 2011) and a central objective of many monitoring and management projects (Groom et al. 2005; Convention on Biological Diversity [CBD], available online ).”
Species Richness, Forest Structure, and Functional Diversity During Succession in the New Guinea Lowlands (2014, methodology): “Differences in stem and species density through succession can influence observed changes in biodiversity (Gotelli & Colwell 2011).”
Soil fertility induces coordinated responses of multiple independent functional traits (2015, methodology): “The Shannon’s evenness index controls for S by dividing Shannon’s H0 diversity index (H0 ¼ PSi¼1 pi ln pi, where pi is the relative basal area of species i) by ln(S) (Magurran & McGill 2011).”
Effective monitoring of freshwater fish (2019, background): “Species richness is the metric most obviously influenced by this, but most biodiversity indices, including, for example, the Berger-Parker dominance metric (Magurran, 2004, 2011; Magurran & McGill, 2011) and Jaccard similarity (Baselga, 2010), are also affected.”
An attribute‐diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures (2019, background): “…as a single number or just a few measures; an enormous number of measures have been proposed to quantify the complementary aspects of trait value distribution among species (Mouchet et al. 2010, Weiher 2011, Swenson et al. 2012, Mason et al. 2013, Mouillot et al. 2013, Scheiner et al. 2017).”
Quantifying sample completeness and comparing diversities among assemblages (2020, background): “Until fairly recently, how to quantify and compare biodiversity across assemblages was one of the most controversial issues in ecology (Magurran & McGill, 2011).”
Effects of human‐modified landscapes on taxonomic, functional and phylogenetic dimensions of bat biodiversity (2015, background): “Rao’s Q is the sum of the distances (functional or phylogenetic) between all possible pairs of species, weighted by the product of their relative abundances, and conceptually is the abundance-weighted average difference among species (Weiher, 2011).”
Thirty years of progeny from Chao’s inequality: Estimating and comparing richness with incidence data and incomplete sampling (2017, background): “…applied to many research fields, not only in ecology and conservation biology, but also in other disciplines; see Chazdon et al. (1998), Magurran (2004), Chao (2005), Gotelli and Colwell (2011), Magurran and McGill (2011), Gotelli and Chao (2013) and Chao and Chiu (2016) for various applications.”
Temporal and spatial variation in distribution of fish environmental DNA in England’s largest lake (2018, methodology): “Finally, sample-based rarefaction (Gotelli & Colwell, 2010)was used to determine the number of samples needed to accurately represent the species assemblage.”
Chebyshev polynomials, moment matching, and optimal estimation of the unseen (2015, result): “We compare our results with the Good-Turing estimator [14], the Chao 1 estimator [5, 16], the two estimators proposed by Chao and Lee [6], and the linear programming approach proposed by Valiant and Valiant [41].”