scholarly journals Using climate, energy, and spatial-based hypotheses to interpret macroecological patterns of North America chelonians

2016 ◽  
Vol 94 (7) ◽  
pp. 453-461 ◽  
Author(s):  
Joshua R. Ennen ◽  
Mickey Agha ◽  
Wilfredo A. Matamoros ◽  
Sarah C. Hazzard ◽  
Jeffrey E. Lovich
Keyword(s):  
Species Richness ◽  
North America ◽  
Spatial Data ◽  
Net Primary Productivity ◽  
Spatial Regression ◽  
Data Sets ◽  
Freshwater Turtles ◽  
Richness Patterns ◽  
Taxonomic Groups ◽  
Broad Scale

Our study investigates how factors, such as latitude, productivity, and several environmental variables, influence contemporary patterns of the species richness in North American turtles. In particular, we test several hypotheses explaining broad-scale species richness patterns on several species richness data sets: (i) total turtles, (ii) freshwater turtles only, (iii) aquatic turtles, (iv) terrestrial turtles only, (v) Emydidae, and (vi) Kinosternidae. In addition to spatial data, we used a combination of 25 abiotic variables in spatial regression models to predict species richness patterns. Our results provide support for multiple hypotheses related to broad-scale patterns of species richness, and in particular, hypotheses related to climate, productivity, water availability, topography, and latitude. In general, species richness patterns were positively associated with temperature, precipitation, diversity of streams, coefficient of variation of elevation, and net primary productivity. We also found that North America turtles follow the general latitudinal diversity gradient pattern (i.e., increasing species richness towards equator) by exhibiting a negative association with latitude. Because of the incongruent results among our six data sets, our study highlights the importance of considering phylogenetic constraints and guilds when interpreting species richness patterns, especially for taxonomic groups that occupy a myriad of habitats.

scholarly journals The pitfalls of biodiversity proxies: Differences in richness patterns of birds, trees and understudied diversity across Amazonia

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Camila D. Ritter ◽  
Søren Faurby ◽  
Dominic J. Bennett ◽  
Luciano N. Naka ◽  
Hans ter Steege ◽  
...  
Keyword(s):  
Species Richness ◽  
Large Scale ◽  
Spatial Scales ◽  
Local Environment ◽  
Life Forms ◽  
Taxonomic Group ◽  
Diversity Gradients ◽  
Richness Patterns ◽  
Taxonomic Groups ◽  
Micro Organisms

AbstractMost knowledge on biodiversity derives from the study of charismatic macro-organisms, such as birds and trees. However, the diversity of micro-organisms constitutes the majority of all life forms on Earth. Here, we ask if the patterns of richness inferred for macro-organisms are similar for micro-organisms. For this, we barcoded samples of soil, litter and insects from four localities on a west-to-east transect across Amazonia. We quantified richness as Operational Taxonomic Units (OTUs) in those samples using three molecular markers. We then compared OTU richness with species richness of two relatively well-studied organism groups in Amazonia: trees and birds. We find that OTU richness shows a declining west-to-east diversity gradient that is in agreement with the species richness patterns documented here and previously for birds and trees. These results suggest that most taxonomic groups respond to the same overall diversity gradients at large spatial scales. However, our results show a different pattern of richness in relation to habitat types, suggesting that the idiosyncrasies of each taxonomic group and peculiarities of the local environment frequently override large-scale diversity gradients. Our findings caution against using the diversity distribution of one taxonomic group as an indication of patterns of richness across all groups.

scholarly journals Testing Seven Hypotheses to Determine What Explains the Current Planthopper (Fulgoridae) Geographical and Species Richness Patterns in China

Insects ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 892
Author(s):  
Zheng-Xue Zhao ◽  
Lin Yang ◽  
Jian-Kun Long ◽  
Zhi-Min Chang ◽  
Zheng-Xiang Zhou ◽  
...  
Keyword(s):  
Species Richness ◽  
Large Scale ◽  
Net Primary Productivity ◽  
Model Averaging ◽  
Ordinary Least Squares ◽  
Environmental Variable ◽  
Vital Role ◽  
Mean Annual Temperature ◽  
Spatial Error ◽  
Richness Patterns

Although many hypotheses have been proposed to understand the mechanisms underlying large-scale richness patterns, the environmental determinants are still poorly understood, particularly in insects. Here, we tested the relative contributions of seven hypotheses previously proposed to explain planthopper richness patterns in China. The richness patterns were visualized at a 1° × 1° grid size, using 14,722 distribution records for 1335 planthoppers. We used ordinary least squares and spatial error simultaneous autoregressive models to examine the relationships between richness and single environmental variables and employed model averaging to assess the environmental variable relative roles. Species richness was unevenly distributed, with high species numbers occurring in the central and southern mountainous areas. The mean annual temperature change since the Last Glacial Maximum was the most important factor for richness patterns, followed by mean annual temperature and net primary productivity. Therefore, historical climate stability, ambient energy, and productivity hypotheses were supported strongly, but orogenic processes and geological isolation may also play a vital role.

scholarly journals Determinants of species richness patterns in the Netherlands across multiple taxonomic groups

2008 ◽  
Vol 18 (1) ◽  
pp. 203-217 ◽  
Author(s):  
M. A. Schouten ◽  
P. A. Verweij ◽  
A. Barendregt ◽  
R. M. J. C. Kleukers ◽  
V. J. Kalkman ◽  
...  
Keyword(s):  
Species Richness ◽  
The Netherlands ◽  
Richness Patterns ◽  
Taxonomic Groups

scholarly journals Determinants and Congruence of Species Richness Patterns across Multiple Taxonomic Groups on a Regional Scale

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Jörn Buse ◽  
Eva Maria Griebeler
Keyword(s):  
Land Use ◽  
Species Richness ◽  
Spatial Patterns ◽  
Regional Scale ◽  
External Validation ◽  
Federal State ◽  
Strongly Correlated ◽  
Richness Patterns ◽  
Taxonomic Groups ◽  
German Federal State

Applying multiple generalized regression models, we studied spatial patterns in species richness for different taxonomic groups (amphibians, reptiles, grasshoppers, plants, mosses) within the German federal state Rhineland-Palatinate (RP). We aimed (1) to detect their centres of richness, (2) to rate the influence of climatic and land-use parameters on spatial patterns, and (3) to test whether patterns are congruent between taxonomic groups in RP. Centres of species richness differed between taxonomic groups and overall richness was the highest in the valleys of large rivers and in different areas of southern RP. Climatic parameters strongly correlated with richness in all taxa whereas land use was less significant. Spatial richness patterns of all groups were to a certain extent congruent but differed between group pairs. The number of grasshoppers strongly correlated with the number of plants and with overall species richness. An external validation corroborated the generality of our species richness models.

scholarly journals Climate and species richness patterns of freshwater fish in North America and Europe

2013 ◽  
Vol 41 (3) ◽  
pp. 452-463 ◽  
Author(s):  
David Griffiths ◽  
Chris McGonigle ◽  
Rory Quinn
Keyword(s):  
Species Richness ◽  
North America ◽  
Freshwater Fish ◽  
Richness Patterns

Human interfaces and urban change

2010 ◽  
Author(s):  
Ned Horning ◽  
Julie A. Robinson ◽  
Eleanor J. Sterling ◽  
Woody Turner ◽  
Sacha Spector
Keyword(s):  
Species Richness ◽  
North America ◽  
Agricultural Land ◽  
Population Change ◽  
Human Impacts ◽  
Remotely Sensed ◽  
Conservation Priorities ◽  
Data Sets ◽  
Remotely Sensed Data ◽  
Political Boundaries

For the first time in human history, more people live in urban areas than in rural areas, and the patterns of suburbanization and urban sprawl once characteristic of North America are now present globally (Obaid 2007). As conservation biologists seek to prioritize conservation efforts worldwide, urbanization and agricultural development emerge as two of the most extensive processes that threaten biodiversity. Suburban and rural sprawl are significant drivers of forest fragmentation and biodiversity loss (e.g., Murphy 1988; Radeloff et al. 2005). Data on human impacts is often averaged across political boundaries rather than biogeographic boundaries, making it challenging to use existing data sets on human demography in ecological studies and relate human population change to the changes in populations of other species. Remotely sensed data can make major contributions to mapping human impacts in ecologically relevant ways. For example, Ricketts and Imhoff (2003) assigned conservation priorities (based on species richness and endemism) for the United States and Canada using several different types of remotely sensed data. For mapping urban cover, they used the map of “city lights at night” from the Defense Meteorological Satellite Program (Imhoff et al. 1997) to classify land as urbanized or not urbanized. For mapping agricultural cover, they used the USGS North America Seasonal Land Cover map (Loveland et al. 2000), derived from the Advanced Very High Resolution Radiometer (AVHRR), lumping five categories to create an agricultural land class. For ecological data, they used a compilation of ecoregion boundaries combined with range maps for over 20,000 species in eight taxa (birds, mammals, butterflies, amphibians, reptiles, land snails, tiger beetles, and vascular plants; Ricketts et al. 1999). Analyzing these data, Ricketts and Imhoff (2003) identified a strong correlation between species richness and urbanization. Of the 110 ecoregions studied, 18 ranked in the top third for both urbanization and biodiversity (species richness, endemism, or both); some of the ecoregions identified as priorities were not identified by a previous biodiversity assessment that did not include the remotely sensed mapping of urbanization (Ricketts et al. 1999).

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