A trait-based approach to assess niche overlap and functional distinctiveness between non-indigenous and native species
Data files
Sep 18, 2023 version files 3.60 MB
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NIS_distinctiveness.zip
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README.md
Abstract
Our understanding of the community assembly processes acting on non-indigenous species (NIS), as well as the relationship with native species is limited, especially in marine ecosystems. To overcome this knowledge gap we here develop a trait-based approach based on the functional distinctiveness metric to assess niche overlap between NIS and native species, using high-resolution data on benthic invertebrate communities in the Baltic Sea. Our results show that NIS retain a certain degree of similarity with native species, but display one or a few singular unique traits (e.g., bioturbation ability). Furthermore, we demonstrate that community assembly processes, including both environmental filtering and limiting similarity affect NIS establishment, but that their effects may be highly context dependent, as illustrated by pronounced spatial patterns in distinctiveness. Finally, our trait-based approach provides a generic framework applicable to other areas and organisms, to better understand and address biological invasions.
README
This README file was generated on 2023-09-13 by Antoni Vivó Pons.
GENERAL INFORMATION
- Title of Dataset: A trait-based approach to assess niche overlap and functional distinctiveness between non-indigenous and native species
- Author Information A. Principal Investigator Contact Information Name: Antoni Vivó Pons Institution: National Insitute for Aquatic resources - Denmark Technical University Address: Kgs. Lyngby, Denmark Email: avipo@aqua.dtu.dk
- Date of data collection (single date, range, approximate date): 2005-2020
- Geographic location of data collection: Baltic Sea, Swedish coast
SHARING/ACCESS INFORMATION
- Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain
- Links to publications that cite or use the data: <br> Vivó-Pons, A., Blomqvist, M., Törnroos, A., & Lindegren, M. (2023). A trait-based approach to assess niche overlap and functional distinctiveness between non-indigenous and native species. Ecology Letters.
- Links to other publicly accessible locations of the data: https://github.com/ToniVP/NIS_distinctiveness
- Links/relationships to ancillary data sets: None
- Was data derived from another source? No
- Recommended citation for this dataset:
Vivó-Pons, A., Blomqvist, M., Törnroos, A., & Lindegren, M. (2023). Data from: A trait-based approach to assess niche overlap and functional distinctiveness between non-indigenous and native species. Dryad Digital Repository.
DATA & FILE OVERVIEW
- File List:
In Raw_data folder (nested within Data folder)
A) species_AFDW_2005_2020.csv
B) species_site_AFDW_2005.csv
C) sp_traits_raw.csv
D) sp_status.csv
E) env_data.csv
In Data folder
F) Di_metrics_station.csv
G) dist_matrix_ovrll.csv
H) sp_traits.csv
I) spe_index.csv
J) Trait_modalities.R
K) traits.effects.csv
- Relationship between files, if important: The Raw_data folder contains all the raw data used to generate the files in the Data folder
- Additional related data collected that was not included in the current data package: None
- Are there multiple versions of the dataset? No
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DATA-SPECIFIC INFORMATION FOR: species_AFDW_2005_2020.csv
- Number of variables: 14
- Number of cases/rows: 101205
- Variable List:
- taxon: all species and genus names included in the analysis
- station: numerical identifier for each unique sampling station
- lon: longitude value (decimal degrees)
- lat: latitude value (decimal degrees)
- year: sampling year (2005-2020)
- month: sampling month
- depth: sampling depth at each station (m)
- sample_number: number of sample taken at each station
- taxon_id: numerical identifier for each taxon
- abundance: number of individuals of each taxon found in that specific sample
- wet_weight: wet weight (g) of each taxon found in that specific sample
- phylum: phylum of the corresponding taxon
- conversion: conversion factor for transforming wet weight to ash-free dry weight (AFDW)
- AFDW: ash-free dry weight (g) of each taxon found in that specific sample
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DATA-SPECIFIC INFORMATION FOR: species_site_AFDW_2005.csv
- Number of variables: 178
- Number of cases/rows: 7118
- Variable List:
- station: numerical identifier for each unique sampling station
- year: sampling year (2005-2020)
- month: sampling month
- lon: longitude value (decimal degrees)
- lat: latitude value (decimal degrees)
- Columns 6 to 178: all taxons' names found in the data with their corresponding AFDW value for each unique sampling event.
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DATA-SPECIFIC INFORMATION FOR: sp_traits_raw.csv
Raw trait modalities information for all species, coded in dummy format (0/1).
- Number of variables: 56
- Number of cases/rows: 173
- Variable List: In this case each column accounts for a specific trait modality (further described in Table 1), showing if a certain taxon displays this modality (1) or not (0).
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DATA-SPECIFIC INFORMATION FOR: sp_status.csv
- Number of variables: 3
- Number of cases/rows: 173
- Variable List:
- taxon: all species and genus names included in the analysis
- phylum: phylum of the corresponding taxon
- status: origin of the taxon (Native or Non-indigenous)
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DATA-SPECIFIC INFORMATION FOR: env_data.csv
Environmental data for each unique sampling event extracted from Copernicus Marine Service (https://marine.copernicus.eu/)
- Number of variables: 13
- Number of cases/rows: 6448
- Variable List:
- station: numerical identifier for each unique sampling station
- year: sampling year (2005-2020)
- lon: longitude value (decimal degrees)
- lat: latitude value (decimal degrees)
- month: sampling month
- year_month: pasted names for the year and month for the corresponding sampling event
- depth: depth (m)
- bot_sal: sea bottom salinity (PSU)
- bot_sal_var: yearly santard deviation for the bottom salinity (PSU)
- bot_T: sea bottom temperature (Celsius)
- bot_T_var: yearly santard deviation for the bottom temperature (Celsius)
- bot_oxy: sea bottom oxygen concentration (mmol/m3)
- bot_oxy_var: yearly santard deviation for the bottom oxygen concentration (mmol/m3)
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DATA-SPECIFIC INFORMATION FOR: Di_metrics_station.csv
Several metrics obtained from the community information at each unique sampling event.
- Number of variables: 37
- Number of cases/rows: 6488
- Variable List:
- station: numerical identifier for each unique sampling station
- year: sampling year (2005-2020)
- lon: longitude value (decimal degrees)
- lat: latitude value (decimal degrees)
- non_NIS_Di: mean functional distinctiveness weighted by AFDW for all native species
- overall_Di: mean functional distinctiveness weighted by AFDW for all species
- ratio_NISvsNAt: relative NIS biomass (%) in the community
- ratio_sp: percentage of NIS in the total number of recorded species within the community
- non_NIS_Di_nw: mean functional distinctiveness not weighted by AFDW for all native species
- overall_Di_nw: mean functional distinctiveness not weighted by AFDW for all species
- nbsp/richness: total species number in the community
- sing.sp: number of functionally singular species in each community
- FRic: Functional Richness of each community
- FEve: Functional Evenness of each community
- FDiv: Functional Divergence of each community
- FDis: Functional Dispersion of each community
- RaoQ: Rao's quadratic entropy (Q) of each community
- Shannon: Shannon's diversity index (H') of each community
- Simpson: Simpson's diversity index (D) of each community
- Evenness: Pielou's species evenness index (J) of each community
- mean_NIS_Di: mean functional distinctiveness weighted by AFDW for all NIS
- NIS name (e.g. "Mya.arenaria"): single NIS values of distinctiveness weighted by AFDW
- NIS name_relab: relative biomass (AFDW) of each NIS
- NIS name_nw: single NIS values of distinctiveness not weighted by AFDW
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DATA-SPECIFIC INFORMATION FOR: dist_matrix_ovrll.csv
Overall pairwise matrix of functional distances between species
- Number of variables: 173
- Number of cases/rows: 173
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DATA-SPECIFIC INFORMATION FOR: sp_traits.csv
Clean species traits data used in the analysis.
- Number of variables: 48
- Number of cases/rows: 173
- Variable List: Column names correspond to specific trait modalities (further described in Table 1) showing if the species display (1) or not (0) that modality. In the case of "Size" and "Adult life span", trait information is coded as ordinal (ranging from 1 to 5).
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DATA-SPECIFIC INFORMATION FOR: spe_index.csv
- Number of variables: 4
- Number of cases/rows: 173
- Variable List:
- taxon: all species and genus names included in the analysis
- int_Di: functional distinctiveness weighted by biomass (AFDW)
- quartile: quartile where each species was classified accoring to their value of distinctiveness (low = 1, high = 4)
- decile: decile where each species was classified accoring to their value of distinctiveness (low = 1, high = 10)
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DATA-SPECIFIC INFORMATION FOR: Trait_modalities.R
List with all the trait and corresponding modalities' names.
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DATA-SPECIFIC INFORMATION FOR: traits.effects.csv
Effect of each trait on regional distinctiveness for each species.
- Number of variables: 11
- Number of cases/rows: 173
- Variable List: In this case, each column name corresponds to the specific trait that has been removed from the computation of distinctiveness. The values correspond to each taxon functional distinctiveness weighted by AFDW without that trait.
CODE INFORMATION
Libraries. List of required libraries.
Functions. List of needed functions.
01_Descriptive_figures. This script contains the code related to the descriptive figures of the data used in the study.
02_Distinctiveness_regional (Step I & II). This script corresponds to the 1st and 2nd steps from the proposed framework, related to the computation of functional distinctiveness of NIS and natives at a regional scale (regional species pool).
03_Distinctiveness_local (Step III). This script corresponds to the 3rd step from the proposed framework, related to the computation of functional distinctiveness of NIS together with other community metrics at a local scale (local species pools).
04_Statistical_analysis (Step III). This script contains all the code related with the multi- and single modeling approaches included in the study to detect potential drivers of NIS distinctiveness at a local scale.
Env_data_extraction. Example of extracting environmental data from several .NC files, downloaded from the Copernicus Marine Service.