Global variation in zooplankton niche divergence across ocean basins
Data files
Jan 21, 2025 version files 153.75 MB
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all_dat_mod.csv
134.20 KB
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bgall.csv
73.38 MB
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env.tif
4.87 MB
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envdat.csv
51.88 MB
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README.md
5.28 KB
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regions.csv
3.22 MB
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SAAR.csv
20.25 MB
Abstract
Modelling responses to climate change assume zooplankton populations remain similar over time with little adaptation (niche conservatism). Oceanic barriers, genetic, phenotypic variation and species interactions in cosmopolitan species could drive niche divergence within species. We assess niche divergence among 224 globally distributed species across the seven main ocean basins. There were 357 diverged niches out of 828 ocean basin comparisons. The proportion of diverged niches varied both across and within phyla. Copepoda (156 of 223 species) were used to test for niche divergence between same species populations across different environmental gradients. Global niche divergence was found to be more likely for species in colder temperatures and near shore environments. Opposing temperature responses were found for four comparisons which may relate to the different connectivity patterns between them. This study demonstrates adaptive potential across environmental-niche gradients, which must be considered when modelling population responses to climate change.
README: Global variation in zooplankton niche divergence across ocean basins
https://doi.org/10.5061/dryad.nvx0k6f2v
Description of the data and file structure
The repository contains code to each of the four main steps of the manuscript. Here we provide a detailed description to replicate the results in the manuscript. We 1) First extract the presences from the 'Zoobase' database and retain species present when n > 50 in two ocean basins, 2) Perform an ensemble model on each species for the global presence distribution in the dataset, 3) Extract observations of a species from paired ocean basin areas to examine niche divergence, 4) Use the results of the global ensemble model and the binary niche divergence/ non-divergence classifier to perform a heriarchical generalised additive model.
Main Data files and RScripts to replicated the analysis in the manuscript:
1. "regions.csv";" glob_species_extract_1.R" Download Zoobase from the zenodo repository
2. "SAAR.csv"; "env.tif"; "envdat.csv"; "Biomod_2.R"
3. "SAAR.csv";"bgall.csv";"env.tif","Niche_overlap_3.R"
4. "all_dat_mod.csv";"HGAM_4.R"
Files and variables
Missing values are assigned "NA"
File: regions.csv
Description: A file used to generate a raster for ocean basins to extract observations from Zoobase
Variables
- x: Longitude
- y: Latitude
- tst: Ocean Basin Code
File: all_dat_mod.csv
Description: The final niche divergence classification of species and paired area comparisons
Variables
- Species: Species name
- phylum: Phylum
- class: Class
- order: Order
- family: Family
- area: Paired area code. Each Ocean basin is defined by 2 character abbreviation. Eg - South Atlantic 'SA' & Arctic Ocean 'AR'. Paired area = 'SA_AR'
- N1: Number of occurrences in area 1after spatial filtering
- N2: Number of occurrences in area 2 after spatial filtering
- DIV: Binary classifier of niche divergence of two populations 'Y' = divergence, 'N' = Conservatism or neutral niches
- ENS.D: Ensemble niche overlap value between 0-1
- ENS.BG1: Mean ensemble background of niche overlap for area 1
- ENS.BG2: Mean ensemble background of niche overlap for area 2
- ENS.SD1: Mean Standard deviation background of niche overlap for area 1
- ENS.SD2: Mean Standard deviation background of niche overlap for area 2
- Bathymetry: Weighted mean niche for species - Bathymetric depth (m)
- Temperature: Weighted mean niche for species - Temperature (°C)
- Chl-a: Weighted mean niche for species - Chlorophyll-a (mg m-3)
- Mixed Layer Depth: Weighted mean niche for species (Mixed Layer Depth (m))
- Nitrate: Weighted mean niche for species - Nitrate (µmol/kg)
- Salinity: Weighted mean niche for species - Salinity (unitless)
- Wind Stress: Weighted mean niche for species - Wind stress (N m-2)
File: SAAR.csv
Description: Species occurrences for those species found with populations in the South Atlantic (SA) and Arctic Ocean (AR)
Variables
- ID: ID number
- x: Longitude
- y: Latitude
- Year: Year of observation
- Month: Month of observation
- Day: Day of observation
- Phylum: Phylum
- Class: Class
- Order: Order
- Family: Family
- Genus: Genus
- Species: Species
- chl: Climatological value of Chlorophyll-a at observation (mg m-3)
- sil: Climatological value of Silicate at observation (µmol/kg)
- sst: Climatological value of Temperature at observation (°C)
- sal: Climatological value of Salinity at observation
- nit: Climatological value of Nitrate at observation(µmol/kg)
- mld: Climatological value of Mixed Layer Depth at observation (m)
- dist: Distance from shoreline for observation (m)
- ws: Climatological value of Wind Stress at observation (N m-2)
- dep: Bathymetry measurement at observation (m)
- oxy: Climatological value of Dissolved Oxygen at observation (µmol/kg)
- bg: Background code used to extract targeted background locations
- obasin: Ocean basin code observation was taken from
File: envdat.csv
Description: The environmental data (climatologies) used to extract background observations for the ensemble modelling using Biomod
Variables
- ID: ID number
- x: Longitude
- y: Latuitude
- M: Month
- chl: Codes as above in SAAR.csv
- sil:
- sst:
- sal:
- nit:
- mld:
- ws:
- dep:
- dist:
- bg:
File: bgall.csv
Description: The background data unrolled climatologies with assigned ocean basins
Variables
- obasin: Codes as above for SAAR.csv
- x:
- y:
- M:
- chl:
- sil:
- sst:
- sal:
- nit:
- mld:
- dist:
- ws:
- dep:
Code/software
R: files
" glob_species_extract_1.R" R scripts to extract species from the zoobase dataset and subset to species present in sufficient numbers within each ocean basin.
"ensemble_2.R" R scripts to perform the ensemble modelling using the R package **biomod2. **We show the process for a subset of 7 species. this is repeated for all 223 species in the database.
"Niche_overlap_3.R" R scripts to perform the background similarity test (niche similarity) between pairs of populations in different ocean basins. Shown here is an example for species with populations in the South Atlantic and Arctic Ocean.
"HGAM_4.R" R scripts to perform the heirarchical generalised additive model.
Methods
Both biological and environmental data are freely available for download.
The zooplankton data are available from the 'Zoobase' dataset https://zenodo.org/records/5101349. This is a collated reseource of all observation records for the main groups of meso-zooplankton
The environmental data were extracted as monthly climatologies from the World Ocean Atlas (2022). Bathymetric depth were extracted from GEBCO’s current gridded bathymetric data set (2024). Monthly climatologies of chlorophyll-a were extracted from the globcolour database (http://www.globcolour.info/).
The repository contains code to each of the four main steps of the manuscript. Here we provide a detailed description to replicate the results in the manuscript. We 1) First extract the presences from the 'Zoobase' database and retain species present when n > 50 in two ocean basins, 2) Perform an ensemble model on each species for the global presence distribution in teh dataset, 3) Extract observations of a species from paired ocean basin areas to examine niche divergence, 4) Use the results of the global ensemble model and the binary niche divergence/ non-divergence classifier to perform a heriarchical generalised additive model.