Species Richness and Evolutionary Niche Dynamics: A Spatial Pattern–Oriented Simulation Experiment

2007 ◽  
Vol 170 (4) ◽  
pp. 602-616 ◽  
Author(s):  
Thiago Fernando L. V. B. Rangel ◽  
José Alexandre F. Diniz‐Filho ◽  
Robert K. Colwell
2007 ◽  
Vol 170 (4) ◽  
pp. 602
Author(s):  
Thiago Fernando L. V. B. Rangel ◽  
José Alexandre F. Diniz-Filho ◽  
Colwell

2009 ◽  
Vol 15 (6) ◽  
pp. 940-947 ◽  
Author(s):  
Tsewang Namgail ◽  
Charudutt Mishra ◽  
Christine B. de Jong ◽  
Sipke E. van Wieren ◽  
Herbert H. T. Prins

2020 ◽  
Author(s):  
Iván Torres ◽  
José M. Moreno

<p>Studying the soil seed bank is a time and space-consuming task, and therefore only a small fraction of the soil is sampled. It is then critical to optimize sampling effort to reliably estimate soil seed bank properties. Here, we test whether the spatial patterns of species richness in the soil seed bank differ under increasing sampling effort. For this, we used data of germination from soils in a mediterranean shrubland in Central Spain. Two data sets were used, one of the seedlings emerging after heating the soil to break dormancy, and one with the combined germinations of heated and non-heated soil subsamples. We simulated increased sampling effort with sample-based rarefaction curves, extrapolating the species richness corresponding to a 2x and 3x increase in the number of individuals (seedlings) per sample. We then analyzed the spatial pattern of the original and extrapolated species richness using linear regression and semivariograms. Species richness increased by 34% and 52% in the 2x and 3x estimations, however the spatial pattern of species richness remained largely unchanged. For the long-distance spatial pattern, the slope of the plot-scale trend (i.e., the regression coefficient) increased only slightly with increasing sampling effort, while the adjusted R-squared of the regression decreased with increasing sampling effort. For the short-distance spatial pattern we could only fit spherical model semivariograms to the data from soils exposed to a heat shock, and the intensity of the spatial pattern (spatial dependence) increased very slightly with increased sampling effort. These results suggest that even with a doubled or tripled sampling effort, as provided by the simulations, the spatial pattern of species richness would have remained unchanged. We argue that increased effort in detecting species in the seed bank needs not necessarily improve the detection of spatial pattern.</p>


2011 ◽  
Vol 100 (2) ◽  
pp. 317-330 ◽  
Author(s):  
Fernando T. Maestre ◽  
Andrea P. Castillo-Monroy ◽  
Matthew A. Bowker ◽  
Raúl Ochoa-Hueso

2004 ◽  
Vol 36 (3-4) ◽  
pp. 249-260 ◽  
Author(s):  
Paweł KAPUSTA ◽  
Grażyna SZAREK-ŁUKASZEWSKA ◽  
Józef KISZKA

The spatial pattern of lichen species richness was analyzed in a forest ecosystem impacted for 50 years by industrial emissions from a steelworks. Geostatistical tools were used to characterize the spatial pattern of the number of lichen species and multiple regression analysis was used to identify factors influencing it. Spatial analysis showed high variation of lichen species richness on a local scale, caused by patchiness of natural habitat factors (species composition of trees, their age, shade, etc.). On a large spatial scale, species richness differentiated the western from the eastern part of the forest. The western part, closer to the sources of pollution, had fewer species (average 6–10 per locality) than the eastern part (10–15 per locality). Multiple regression analysis was used to examine the relationships between the species richness of lichens and several environmental variables: input of ions with bulk precipitation (SO42−, NO−3, Cl−, Ca2+, Mg2+, Fe3+, Zn3+, Pb2+, Cd2+), distance to forest edge, tree stand age, and number of species per locality. Regression analysis was preceded by factor analysis for the input of ions to obtain uncorrelated variables. Regression explained 53% of the variation of lichen species richness. Highly significant predictor variables were the factor connected with the input of pollutants (Fe3+, Zn2+) emitted by the steelworks (negative effect) and the number of trees per locality (positive effect). Species richness was also affected by the age structure of the tree stand; more species were recorded in old forests.


2007 ◽  
Vol 250 (1-2) ◽  
pp. 64-70 ◽  
Author(s):  
Mari Moora ◽  
Tim Daniell ◽  
Heikki Kalle ◽  
Jaan Liira ◽  
Kersti Püssa ◽  
...  

2009 ◽  
Vol 17 (3) ◽  
pp. 272 ◽  
Author(s):  
Li Guo ◽  
Shen Zehao ◽  
Ying Tsunshen ◽  
Fang Jingyun

Diversity ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 96
Author(s):  
Yao Chi ◽  
Jiechen Wang ◽  
Changbai Xi ◽  
Tianlu Qian ◽  
Caiying Sheng

We describe large-scale patterns of terrestrial mammal distribution in China by using geographical information system (GIS) spatial analysis. Mammal taxa, examined by species, family, and order, were binned into 10 km × 10 km grids to explore the relationship between their spatial distribution and geographical factors potentially affecting the same. The spatial pattern of species richness revealed four agglomerations: high richness in the south, low in north, and two low richness areas in eastern and western China. Species richness patterns in Carnivora was the most similar to overall terrestrial mammals’ richness; however, species richness in different orders exhibited distributions distinct from the overall pattern. We found a negative relationship between richness and latitude gradient. Species richness was most strongly correlated with forested ecosystems, and was found to be higher at an elevation of 2000~2200 m, with greater altitudinal variation indicative of higher species richness.


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