scholarly journals Temporal and spatial variation of water quality in Yuqiao Reservoir induced by water diversion project

2019 ◽  
Vol 31 (1) ◽  
pp. 52-64
Author(s):  
LIU Changjie ◽  
◽  
YU Minghui ◽  
ZHOU Chaohui ◽  
WU Ruilin
Keyword(s):  
Water Quality ◽  
Spatial Variation ◽  
Water Diversion ◽  
Temporal And Spatial Variation ◽  
Yuqiao Reservoir ◽  
Water Diversion Project ◽  
Temporal And Spatial

scholarly journals Phylogenetic paleoecology: macroecology within an evolutionary framework

Paleobiology ◽  
2021 ◽  
Vol 47 (2) ◽  
pp. 171-177
Author(s):  
James C. Lamsdell ◽  
Curtis R. Congreve
Keyword(s):  
Species Diversity ◽  
Spatial Variation ◽  
Geographic Distribution ◽  
Evolutionary Biology ◽  
Phylogenetic Signal ◽  
Evolutionary Rates ◽  
The Other ◽  
Ecological Data ◽  
Temporal And Spatial Variation ◽  
Temporal And Spatial

The burgeoning field of phylogenetic paleoecology (Lamsdell et al. 2017) represents a synthesis of the related but differently focused fields of macroecology (Brown 1995) and macroevolution (Stanley 1975). Through a combination of the data and methods of both disciplines, phylogenetic paleoecology leverages phylogenetic theory and quantitative paleoecology to explain the temporal and spatial variation in species diversity, distribution, and disparity. Phylogenetic paleoecology is ideally situated to elucidate many fundamental issues in evolutionary biology, including the generation of new phenotypes and occupation of previously unexploited environments; the nature of relationships among character change, ecology, and evolutionary rates; determinants of the geographic distribution of species and clades; and the underlying phylogenetic signal of ecological selectivity in extinctions and radiations. This is because phylogenetic paleoecology explicitly recognizes and incorporates the quasi-independent nature of evolutionary and ecological data as expressed in the dual biological hierarchies (Eldredge and Salthe 1984; Congreve et al. 2018; Fig. 1), incorporating both as covarying factors rather than focusing on one and treating the other as error within the dataset.

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