Data Package Metadata   View Summary

Correlation of native and exotic species richness: a global meta-analysis finds no invasion paradox across scales

General Information
Data Package:
Local Identifier:edi.548.1
Title:Correlation of native and exotic species richness: a global meta-analysis finds no invasion paradox across scales
Alternate Identifier:DOI PLACE HOLDER
Abstract:

This data set is also available on the Dryad Digital repository (link : https://doi.org/10.5061/dryad.59kv753)

This dataset was created for investigating the biotic resistance hypothesis, that is, the idea that species‐rich communities are more successful at resisting invasion by exotic species than are species‐poor communities. It has been argued that native–exotic richness relationships (NERR) are negative at small spatial scales and positive at large scales, but evidence for the role of spatial scale on NERR has been contradictory. However, no formal quantitative synthesis had previously examined whether NERR is scale‐dependent across multiple studies, and previous studies on NERR have not distinguished spatial grain and extent, which may drive very different ecological processes Therefore, a global systematic review was carried out to create this dataset, which includes 204 individual cases of observational (non‐experimental) NERRs from 101 publications. Further, the above-mentioned hypotheses were investigated using a hierarchical mixed‐effects meta‐analysis, which showed that NERR was indeed highly scale dependent across studies and increased with the log of grain size. Also, no clear patterns of NERR across different spatial extents were found, suggesting that extent plays a less important role in determining NERR than does grain, although there was a complex interaction between extent and grain size. Almost all studies on NERR were found to have been conducted in North America, western Europe, and a few other regions, with little information on tropical or Arctic regions. NERR was also found to increase northward in temperate regions and vary with longitude. These results were published in the paper titled Correlation of native and exotic species richness: a global meta‐analysis finds no invasion paradox across scales (Peng et al. 2018), which represents the first global quantitative analysis of scale‐based NERR.

Publication Date:2020-06-18

Time Period
Begin:
1994
End:
2018

People and Organizations
Contact:Gurevitch, Jessica (Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York, 11794, USA) [  email ]
Creator:Peng, Shijia (State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, China)
Creator:Kinlock, Nicole L. (Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York, 11794, USA)
Creator:Gurevitch, Jessica (Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York, 11794, USA)
Creator:Peng, Shaolin (State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, China)

Data Entities
Data Table Name:
DataS1_SystematicReviewDatabase.csv
Description:
data used only in the systematic review, not the meta-analysis. Each entry describes a single case.
Data Table Name:
DataS2_MetaAnalysisDatabase.csv
Description:
data used in all meta-analyses. Each entry describes a single case, and each case includes all covariates used in meta-analytic models.
Data Table Name:
DataS3_ExperimentalStudiesDatabase.csv
Description:
data from experimental studies that were not included in meta-analyses. These studies were, however, included in the systematic review.
Other Name:
RCodeS2_MetaAnalysis.R
Description:
R code used to run all analyses.
Detailed Metadata

Data Entities


Data Table

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Number of Columns:9

Table Structure
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Table Column Descriptions
 
Column Name:AuthorYear  
Year  
Grain  
Grain_units  
Latitude  
Longitude  
Extent  
ResearchType  
HabitatType  
Definition:Last name of the first author and year of publicationYear of publicationGrain size, that is, the smallest area for which data is available, or sampling unit/plot sizeUnits of grain sizeLatitude in degree decimal (positive = North, negative = South)Longitude in degree decimal (positive = East, negative = West)Total spatial area included in the study (see table 1 in methods for levels)Whether the study was experimental or observationalThe type of habitat in which the study was carried out (see table in methods for levels).
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Min1997 
Max2015 
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Codem2
Definitionsquare meter
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Code Definition
Codeha
Definitionhectare
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DefinitionLatitude in degree decimal (positive = North, negative = South)
DefinitionLongitude in degree decimal (positive = East, negative = West)
DefinitionTotal spatial area included in the study (see table 1 in methods for levels)
DefinitionWhether the study was experimental or observational
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Code Definition
Codeforest
Definitionforest
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Code Definition
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Code Definition
Coderiparian
Definitionriparian
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Code Definition
Codeagricultural
Definitionagricultural
Source
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Data Table

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Name:DataS2_MetaAnalysisDatabase.csv
Description:data used in all meta-analyses. Each entry describes a single case, and each case includes all covariates used in meta-analytic models.
Number of Records:204
Number of Columns:14

Table Structure
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Table Column Descriptions
 
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Study  
Case  
Year  
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Grain_unit  
ln_Grain  
Latitude  
Longitude  
Extent  
Research_type  
Habitat_type  
z  
var_z  
Definition:Last name of the first author and year of publicationA unique identification number for each case within a studyA unique identification number for each studyYear of studyGrain size, that is, the smallest area for which data is available, or sampling unit/plot sizeUnits of grain sizeNatural logarithm of grain in MetresSquaredLatitude in degree decimal (positive = North, negative = South)Longitude in degree decimal (positive = East, negative = West)Total spatial area included in the study (see table in methods for levels)Whether the study was experimental or observationalThe type of habitat in which the study was carried out (see table in methods for levels).The value of the Fischer’s z transformation of the Pearson’s product moment correlation coefficient of the respective case. This was used as effect size in the meta-analysisThe variance of Fischer’s z
Storage Type:string  
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DefinitionA unique identification number for each study
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Max2016 
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Typereal
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Max23716 
Allowed Values and Definitions
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Definitionsquare meter
Source
Code Definition
Codekm2
Definitionsquare kilometer
Source
Code Definition
Codeha
Definitionhectare
Source
UnitsquareMeter
Typereal
Min-4.6052 
Max20.7947 
Unitdegree
Typereal
Min-57.38 
Max64 
Unitdegree
Typereal
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Source
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Code[104-105]
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Code Definition
Code[0-10]
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Code Definition
Code[106- ]
Definitionover 10^6 square kilometer
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DefinitionWhether the study was experimental or observational
Allowed Values and Definitions
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Code Definition
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Code Definition
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Codeurban
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Codeshrubland
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Accuracy Report:                            
Accuracy Assessment:                            
Coverage:                            
Methods:                            

Data Table

Data:https://pasta-s.lternet.edu/package/data/eml/edi/548/1/0c0849b00e2bf492909091a38710bf18
Name:DataS3_ExperimentalStudiesDatabase.csv
Description:data from experimental studies that were not included in meta-analyses. These studies were, however, included in the systematic review.
Number of Records:14
Number of Columns:11

Table Structure
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Table Column Descriptions
 
Column Name:AuthorYear  
Year  
Grain_m2  
Extent  
Habitat_type  
n  
NERR_sign  
Primary_data_used_for_meta_analysis  
r  
z  
var_z  
Definition:Last name of of first author and year of publicationYear of publicationGrain size, that is, the smallest area for which data is available/sampling unit or plot sizeExtent, that is, total spatial area included in the study (see table in methods for levels)The type of habitat in which the study was carried out (see table in methods for levels).Sample size of each group in the studyWhether the correlation between native species richness and exotic species richness is positive or negativeWhether the data was used for the meta-analysisthe value of the Pearson’s product moment correlation rThe value of the Fischer’s z transformation of the Pearson’s product moment correlation coefficient of the respective case.The variance of Fischer’s z
Storage Type:string  
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Typenatural
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Max2015 
UnitsquareMeter
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Max
Allowed Values and Definitions
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Code Definition
Code[0-10]
Definition0-10 square kilometer
Source
Allowed Values and Definitions
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Code Definition
Codegrassland
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Source
Code Definition
Coderiparian(grassland)
Definitionriparian(grassland)
Source
Code Definition
Codesavanna
Definitionsavanna
Source
Unitnumber
Typenatural
Min
Max204 
DefinitionWhether the correlation between native species richness and exotic species richness is positive or negative
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CodeYes
Definitionused in meta analysis
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Code Definition
CodeNo
DefinitionNot usd in meta analysis
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Entity Type:unknown
Description:R code used to run all analyses.
Physical Structure Description:
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Size:8598 bytes
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Data:https://pasta-s.lternet.edu/package/data/eml/edi/548/1/398fed55684ef18f9590332954a25bc9

Data Package Usage Rights

This information is released under the Creative Commons license - Attribution - CC BY (https://creativecommons.org/licenses/by/4.0/). The consumer of these data (\"Data User\" herein) is required to cite it appropriately in any publication that results from its use. The Data User should realize that these data may be actively used by others for ongoing research and that coordination may be necessary to prevent duplicate publication. The Data User is urged to contact the authors of these data if any questions about methodology or results occur. Where appropriate, the Data User is encouraged to consider collaboration or co-authorship with the authors. The Data User should realize that misinterpretation of data may occur if used out of context of the original study. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. All data are made available \"as is.\" The Data User should be aware, however, that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. The data authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data. Thank you.

Keywords

By Thesaurus:
(No thesaurus)Community patterns, meta-analysis
LTER Controlled Vocabularyinvasive species, plant species, species richness

Methods and Protocols

These methods, instrumentation and/or protocols apply to all data in this dataset:

Methods and protocols used in the collection of this data package
Description:

Systematic review

A systematic review was conducted in February 2017 for scale‐dependent NERR in plants using the keywords “biodiversity or diversity” AND “plant and inva*” in Web of Science (ISI) and Google Scholar from 1986 to 2016, limiting our search results to relevant research fields of “Ecology,” “Plant sciences,” and “Biodiversity conservation.” The Chinese National Knowledge Infrastructure (CNKI) database was also searched for relevant papers but, regrettably, no articles found in CNKI were suitable. We then screened the titles, abstracts, and results to select studies based on the following criteria: (1) Studies were observational or experimental. Literature reviews, syntheses and mathematical simulation models were excluded. (2) Studies reported NER using species richness; studies reporting other response indices (e.g., species cover, density, or abundance; biomass of one or several exotic species) were excluded. Relevant publications were further screened using the full text. We also cross‐checked studies derived from the reference lists of relevant reviews and articles we previously identified to investigate whether there were additional publications. For our systematic review, we extracted descriptive information from each study to catalog its characteristics. If a study identified study location by name only and did not provide concrete geographic data, we used Google Maps to estimate latitude and longitude. When the study area spanned a large region (e.g., California), we used the midpoint of the region. These midpoints were automatically calculated by using a Geographic Midpoint Calculator (http://www.geomidpoint.com). Few studies provided explicit values for spatial extent. To approximate the extent of study areas, we selected the four most distant points sampled in each study at the cardinal directions of the study area, calculated their distances with ImageJ software, and estimated the rectangular area (Schneider et al. 2012; available online). Extent estimates were grouped into seven categories, as shown in the table below:

Case characteristics and Levels

Publication journal and year:

Grain size: classified into six categories: (0, 1], (1, 10], (10, 100], (100, 500], (500, 1,000] and (1,000) m2

Country:

Study area within country:

Geographic coordinates: midpoint of study area (latitude, longitude)

Spatial extent: (0, 10), [10, 100), [10^2, 10^3), [10^3, 10^4), [10^4, 10^5), [10^5, 10^6) and [10^6) km2

Research type: observational or experimental

Habitat type: forest, grassland, shrubland, wetland, riparian, savanna, agricultural habitat (refers to the non‐crop semi‐natural areas in an agricultural landscape), urban, many habitat types included in study, and miscellaneous other habitats (e.g., freshwater, old fields)

Climatic zone: tropical, temperate, or polar

Meta‐analysis

Our meta‐analysis used a subset of studies from the systematic review for which the correlation between native and exotic (or alien/invasive) species richness was explicitly reported or could be calculated across multiple grain sizes. We extracted Pearson's product–moment correlation coefficients (r) directly from the study, if available, or we calculated r using native and exotic species richness if reported for multiple plots, extracting data points from figures if needed with Getdata 2.26

Because almost all of the experimental studies included in our systematic review were conducted in grasslands at small grain sizes and extents, we limited the meta‐analysis to natural (i.e., unmanipulated) communities rather than including experimental plant communities. Some studies incorporated more than one NERR in different habitat types, locations, or at different spatial extents. For those studies, these NERR values were included as separate individual observations (cases). We only considered neophytes to be exotic (Deutschewitz et al. 2003). We did not extract data from state species lists or other sources of this type directly, because those data do not provide information on grain sizes (Vitousek et al. 1997, Wu et al. 2010).

Other details in the data collecting processes:

Many studies selected several sites (with specific coordinates) within a larger region. If they reported results separately for each site, we regarded results from each site as an individual case (Belote et al. 2008). For a minority of studies that attempted to explore the effects of artificial disturbances on NERRs, if they reported results of both undisturbed and disturbed (or both pre-disturbance and post-disturbance) plots, only undisturbed (or pre-disturbance) plots were used (McGranahan et al. 2012), and if studies reported temporal dynamic change of NERRs under a disturbance regime over time, we selected the earliest measurement (Meiners et al. 2002). Some studies, especially for nested design surveys, reported both cumulative diversity (gamma diversity) and mean diversity observed at a certain grain size (Jauni and Hyvönen 2012). Researchers generally focus on actual species counts rather than aggregated means when calculating NERRs (Deutschewitz et al. 2003, Belote et al. 2008, Chen et al. 2010). We therefore used only cumulative species richness counts as long as the sample plots were separate and independent of each other.

People and Organizations

Creators:
Individual: Shijia Peng
Organization:State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, China
Email Address:
pengshijia1010@163.com
Individual: Nicole L. Kinlock
Organization:Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York, 11794, USA
Email Address:
nicole.kinlock@stonybrook.edu
Id:https://orcid.org/0000-0002-2917-5133
Individual: Jessica Gurevitch
Organization:Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York, 11794, USA
Email Address:
jessica.gurevitch@stonybrook.edu
Id:https://orcid.org/0000-0003-0157-4332
Individual: Shaolin Peng
Organization:State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, China
Email Address:
lsspsl@mail.sysu.edu
Contacts:
Individual: Jessica Gurevitch
Organization:Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York, 11794, USA
Email Address:
jessica.gurevitch@stonybrook.edu
Id:https://orcid.org/0000-0003-0157-4332

Temporal, Geographic and Taxonomic Coverage

Temporal, Geographic and/or Taxonomic information that applies to all data in this dataset:

Time Period
Begin:
1994
End:
2018
Geographic Region:
Description:Global
Bounding Coordinates:
Northern:  80Southern:  -80
Western:  -180Eastern:  180

Project

Parent Project Information:

Title:Study on the Resistance Mechanism of Different Plant Functional Groups to Alien Plant Invasion
Personnel:
Individual: Shaolin Peng
Role:Principal Investigator
Funding: National Natural Science Foundation of China (NSFC) 31030015

Maintenance

Maintenance:
Description:completed
Frequency:
Other Metadata

EDI is a collaboration between the University of New Mexico and the University of Wisconsin – Madison, Center for Limnology:

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