DOI: 10.5061/dryad.4mw6m90gn
title: A trait-based approach to assess niche overlap and functional distinctiveness between non-indigenous and native species

description: 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. 
description: This README file was generated on 2023-09-13 by Antoni Vivó Pons. GENERAL
 INFORMATION 1. Title of Dataset: A trait-based approach to assess niche
 overlap and functional distinctiveness between non-indigenous and native
 species 2. 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 3. Date of data collection (single date,
 range, approximate date): 2005-2020 4. Geographic location of data
 collection: Baltic Sea, Swedish coast SHARING/ACCESS INFORMATION 1.
 Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0)
 Public Domain 2. 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. 3. Links to other publicly accessible locations of the
 data: https://github.com/ToniVP/NIS\_distinctiveness 4.
 Links/relationships to ancillary data sets: None 5. Was data derived from
 another source? No 6. 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 1. 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 2. Relationship between files,
 if important: The *Raw\_data* folder contains all the raw data used to
 generate the files in the *Data* folder 3. Additional related data
 collected that was not included in the current data package: None 4. Are
 there multiple versions of the dataset? No
 #########################################################################
 DATA-SPECIFIC INFORMATION FOR: species\_AFDW\_2005\_2020.csv 1. Number of
 variables: 14 2. Number of cases/rows: 101205 3. 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
 #########################################################################
 DATA-SPECIFIC INFORMATION FOR: species\_site\_AFDW\_2005.csv 1. Number of
 variables: 178 2. Number of cases/rows: 7118 3. 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.
 #########################################################################
 DATA-SPECIFIC INFORMATION FOR: sp\_traits\_raw.csv Raw trait modalities
 information for all species, coded in dummy format (0/1). 1. Number of
 variables: 56 2. Number of cases/rows: 173 3. 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).
 #########################################################################
 DATA-SPECIFIC INFORMATION FOR: sp\_status.csv 1. Number of variables: 3 2.
 Number of cases/rows: 173 3. 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)
 #########################################################################
 DATA-SPECIFIC INFORMATION FOR: env\_data.csv Environmental data for each
 unique sampling event extracted from Copernicus Marine Service
 ([https://marine.copernicus.eu/](https://marine.copernicus.eu/)) 1. Number
 of variables: 13 2. Number of cases/rows: 6448 3. 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)
 #########################################################################
 DATA-SPECIFIC INFORMATION FOR: Di\_metrics\_station.csv Several metrics
 obtained from the community information at each unique sampling event. 1.
 Number of variables: 37 2. Number of cases/rows: 6488 3. 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
 #########################################################################
 DATA-SPECIFIC INFORMATION FOR: dist\_matrix\_ovrll.csv Overall pairwise
 matrix of functional distances between species 1. Number of variables: 173
 2. Number of cases/rows: 173
 #########################################################################
 DATA-SPECIFIC INFORMATION FOR: sp\_traits.csv Clean species traits data
 used in the analysis. 1. Number of variables: 48 2. Number of cases/rows:
 173 3. 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).
 #########################################################################
 DATA-SPECIFIC INFORMATION FOR: spe\_index.csv 1. Number of variables: 4 2.
 Number of cases/rows: 173 3. 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)
 #########################################################################
 DATA-SPECIFIC INFORMATION FOR: Trait\_modalities.R List with all the trait
 and corresponding modalities' names.
 #########################################################################
 DATA-SPECIFIC INFORMATION FOR: traits.effects.csv Effect of each trait on
 regional distinctiveness for each species. 1. Number of variables: 11 2.
 Number of cases/rows: 173 3. 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](https://github.com/ToniVP/NIS_distinctiveness/blob/main/Code/Libraries.R). List of required libraries. [Functions](https://github.com/ToniVP/NIS_distinctiveness/blob/main/Code/Functions.R). List of needed functions. [01_Descriptive_figures](https://github.com/ToniVP/NIS_distinctiveness/blob/main/Code/01_Descriptive%20figures.R). This script contains the code related to the **descriptive figures of the data** used in the study. [02_Distinctiveness_regional (Step I & II)](https://github.com/ToniVP/NIS_distinctiveness/blob/main/Code/02_Distinctiveness_regional%20(Step%20I%20%26%20II).R). 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)](https://github.com/ToniVP/NIS_distinctiveness/blob/main/Code/03_Distinctiveness_local%20(Step%20III).R). 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)](https://github.com/ToniVP/NIS_distinctiveness/blob/main/Code/04_Statistical_analysis%20(Step%20III).R). 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](https://github.com/ToniVP/NIS_distinctiveness/blob/main/Code/Env_data_extraction.R). Example of extracting environmental data from several .NC files, downloaded from the [Copernicus Marine Service](https://data.marine.copernicus.eu/products).