Climate warming shifts riverine macroinvertebrate communities to be more sensitive to chemical pollutants
Data files
May 06, 2024 version files 82.74 MB
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jaccard_pairwise.csv
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jaccard_similarity_25km.csv
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NMDS_site_scores.csv
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NMDS_taxa_scores.csv
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PAF_RICT_25km_all_chems.csv
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r2_sig.csv
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README.md
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site_sprn_0-4_25km_taxonomy.csv
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site_sprn_0-4_25km.csv
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taxa_change_sites_long.csv
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taxa_change_sites_matrix.csv
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taxa_change_sites.csv
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temp_change_25km_rcp26_2090.csv
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temp_change_25km_rcp45_2090.csv
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temp_change_25km_rcp60_2090.csv
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temp_change_25km_rcp85_2090.csv
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tox_data.zip
Abstract
Freshwaters are highly threatened ecosystems that are vulnerable to chemical pollution and climate change. Freshwater taxa vary in their sensitivity to chemicals and changes in species composition can potentially affect the sensitivity of assemblages to chemical exposure. Here we explore the potential consequences of future climate change on the composition and sensitivity of freshwater macroinvertebrate assemblages to chemical stressors using the UK as a case study.
Macroinvertebrate assemblages under end of century (2080-2100) and baseline (1980-2000) climate conditions were predicted for 608 UK sites for four climate scenarios corresponding to mean temperature changes of 1.28°C to 3.78°C. Freshwater macroinvertebrate toxicity data were collated for 19 chemicals and the hierarchical Species Sensitivity Distribution (hSSD) model was used to predict the sensitivity of untested taxa using relatedness within a Bayesian approach. All four future climate scenarios resulted in shifts in assemblage composition, with increases in the prevalence of molluscan, crustacean and annelid species, and towards increasing insect taxa of Odonata, Chironomidae, and Baetidae species. In contrast decreases in were projected for Plecoptera, Ephemeroptera (except for Baetidae) and Coleoptera species.
Shifts in taxonomic composition were associated with changes in the percentage of species at risk from chemical exposure. For the 3.78°C climate scenario, 76% of all assemblages became more sensitive to chemicals and for 18 of the 19 chemicals, the percentage of species at risk increased. Climate warming-induced increases in sensitivity were greatest for assemblages exposed to metals and were dependent on baseline assemblage composition, which varied spatially.
Climate warming is predicted to result in changes in the use, environmental exposure and toxicity of chemicals. Here we show that, even in the absence of these climate-chemical interactions, shifts in species composition due to climate warming will increase chemical risk and that the impact of chemical pollution on freshwater macroinvertebrate biodiversity may double or quadruple by the end of the 21st century.
README: Climate warming shifts riverine macroinvertebrate communities to be more sensitive to chemical pollutants
Access these data on https://doi.org/10.5061/dryad.zs7h44jhb
Software/code available at https://doi.org/10.5281/zenodo.10811583
Tom Sinclair1, Peter Craig2 and Lorraine Maltby1
1 Department of Biosciences, University of Sheffield, Alfred Denny Building, Western Bank, Sheffield, S10 2TN, United Kingdom
2 Department of Mathematical Sciences, Durham University, Upper Mountjoy Campus, Stockton Road, Durham DH1 3LE, United Kingdom
This work was supported by the award of a NERC (Natural Environment Research Council) studentship to TMS (1945243) via the ACCE (Adapting to the Challenges of a Changing Environment) Doctoral Training Programme. NERC is a section of the UKRI (United Kingdom Research and Innovation).
Data file structure
Missing values in data are denoted by NA. The files have been broken down to the step in pipeline they are produced and used
UKCP18:
temp_change_25km_rcp26_2090.csv
temp_change_25km_rcp45_2090.csv
temp_change_25km_rcp60_2090.csv
temp_change_25km_rcp85_2090.csv
All of the UKCP18 output files follow the same format for each of the climate scenarios of RCP2.6, RCP4.5, RCP6.0 and RCP8.5 as reflected in the file titles. Where a value for the site could not be determined due to the scale of the UKCP18 data a value of 9.96920996838687E+36 was inputted as copied from MetOffice (2019)
site - RICT site reference code
temp - change in daily average air temperature in oC between the baseline (1981-2000) and the future (2080–2099)
temp_max - change in daily maximum air temperature in oC between the baseline (1981-2000) and the future (2080–2099)
temp_max - change in daily minumum air temperature in oC between the baseline (1981-2000) and the future (2080–2099)
temp_max - change in daily range of air temperature in oC between the baseline (1981-2000) and the future (2080–2099), a negative value indicates a decrease in the range compared to the baseline
RICT:
jaccard pairwise.csv
Jaccard similarity of the taxonomy present between pairs of sites
scenario - the site when under the baseline (base) or a future (fute_rcpXX, where XX refers to the scenario number of RCP2.6, RCP4.5, RCP6.0 and RCP8.5) scenario
site_A - RICT site reference code
site_B - RICT site reference code site A is being compared to
jaccard - Jaccard similarity between site A and site B
jaccard similarity.csv
Jaccard similarity for the taxonomy of a site temporally between the baseline and each of the four future scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP8.5)
site - RICT site reference code
prop_base_rcp26 - Jaccard index for taxonomy calcualted between the site under baseline and future RCP2.6
prop_base_rcp45 - Jaccard index for taxonomy calcualted between the site under baseline and future RCP4.5
prop_base_rcp60 - Jaccard index for taxonomy calcualted between the site under baseline and future RCP6.0
prop_base_rcp85 - Jaccard index for taxonomy calcualted between the site under baseline and future RCP8.5
NMDA_site_scores.csv
A list of the calculated NMDS scores based on the taxonomy present for each site and scenario combination
NMDS1 - the NMDS1 axis score
NMDS2 - the NMDS2 axis score
site_scenario - the RICT site code and scenario for which the taxonomy has been used to calculate NMDS values
site - just the site from site_scenario, RICT site reference code
scenario - just the scenario from site_scenario, the baseline (base) or a future (rcpXX, where XX refers to the scenario number of RCP2.6, RCP4.5, RCP6.0 and RCP8.5) scenario
NMDS_taxa_scores.csv
A list of the average NMDS axis scores for each taxa across all the sites where that taxa is present
taxa - the taxonomic name of the taxa
NMDS1 - the NMDS1 axis score
NMDS2 - the NMDS2 axis score
site_sprn_0-4.csv
A list of RICT sites and their predicted taxa when run through RICT under baseline conditions and for each future scenario and selected by a 40% occurence rate cut-off as according to
site - the RICT site code
taxa - the taxonomic name of the taxa predicted by RICT
scenario - the site when under the baseline (base) or a future (fute_rcpXX, where XX refers to the scenario number of RCP2.6, RCP4.5, RCP6.0 and RCP8.5) scenario
site_sprn_0-4_25km_taxonomy.csv
The Linnean taxonomic ranking for all the taxa predicted by RICT
Kingdom - kingdom of the taxa
Phylum_division - phylum of the taxa
Subphylum - taxonomic level between phylum and class; above superclass
Subphylum - taxonomic level between phylum and class; below subphylum
Class - class of the taxa
Order - order of the taxa
Family - family of the taxa
Genus - genus of the taxa
Latin - species exclusing the genus of the taxa
taxa_change_sites.csv
A presence absence list for each site and taxa combination as predicted by RICT for each climate scenario
site - the RICT site code
taxa - the taxonomic name of the taxa predicted by RICT
base - presence or absence of the taxa at that site under the baseline scenario
fute_rcp26 - presence or absence of the taxa at that site under the future RCP2.6 scenario
fute_rcp45 - presence or absence of the taxa at that site under the future RCP4.5 scenario
fute_rcp60 - presence or absence of the taxa at that site under the future RCP6.0 scenario
fute_rcp8.5 - presence or absence of the taxa at that site under the future RCP8.5 scenario
taxa_change_sites_long.csv
A presence absence list for each site and taxa combination as predicted by RICT for each climate scenario in long format which also includes some higher taxonomic rankings, whether that taxa is gained or lost compared to the baseline and some clade groupings used for plotting and data analysis
site - the RICT site code
taxa - the taxonomic name of the taxa predicted by RICT
phylum - phylum of the taxa
class - class of the taxa
order - order of the taxa
scenario - the site when under a future (fute_rcpXX, where XX refers to the scenario number of RCP2.6, RCP4.5, RCP6.0 and RCP8.5) scenario
presence - a binary 1 or 0 refering to the presence (1) or absence (0) of a taxa at that site and scenario combination
change - A gain (Gain), loss (Loss) or no change (NA) in the presence of that taxa at that site for that scenario when compared to the baseline
clade - taxonomic grouping used for plotting and data analysis
taxa_change_sites_matrix.csv
The complete presence absence matrix used to fit the NMDS for taxa and the corresponding site and scenario combination
site_scenario - the RICT site code and scenario for which the taxonomy has been used to calculate NMDS values, the scenario is either a baseline (base) or a future (rcpXX, where XX refers to the scenario number of RCP2.6, RCP4.5, RCP6.0 and RCP8.5) scenario
all other column names refer to each of the taxa predicted by RICT (full taxonomy listed in site_sprn_0-4_25km_taxonomy.csv)
hSSD:
tax_merged.csv
The Linnean taxonomic ranking for all the taxa predicted by RICT or present within the toxicity datasets
Kingdom - kingdom of the taxa
Phylum_division - phylum of the taxa
Subphylum - taxonomic level between phylum and class; above superclass
Subphylum - taxonomic level between phylum and class; below subphylum
Class - class of the taxa
Order - order of the taxa
Family - family of the taxa
Genus - genus of the taxa
Latin - species exclusing the genus of the taxa
r2_sig.csv
R squared values for the leave one out test for each of the 19 chemicals considered and whether the R squared value was significantly different from 0
chem - chemical abbreviation, the following chemicals (followed by addreviation used in file name) are included:
azinphos-methyl,AZM
carbofuran,CBF
carbaryl,CBL
cadmium,Cd
copper, Cu
DDT,DDT
deltamethrin,DTM
diazinon,DZN
endosulfan,EDS
fenitrothion,FNT
lead,Pb
lindane,LIN
malathion,MLT
methoxychlor,MXC
nickel,Ni
parathion-ethyl,PAE
parathion-methyl,PAM
pentachlorophenol,PCP
zinc, Zn
model - the version of the hSSD model database used for predictions
r2 - R squared value comparing the predicted against actual toxicity values for each taxa with sensitivity data for every chemical
n - number of taxa with sensitivity data for that chemical
t -the t-value of the test to assess significance of the R squared compared to 0
crit - the critical t-value for a dataset of size name
sig - whether the chemical dataset is significantly (sig) or not (not sig) different from 0
PAF_RICT_25km_all_chems.csv
The list of proportion affect fraction (PAF) for every site and scenario combination
site - the RICT site code
HC5 - the calculated hazardous concentration affecting 5% of taxa for this site and scenario
lower - the 95% lower confidence interval for the calculated hazardous concentration affecting 5% of taxa for this site and scenario
HC5_50th - the 50th percentile for the calculated hazardous concentration affecting 5% of taxa for this site and scenario
upper - the 95% upper confidence interval for the calculated hazardous concentration affecting 5% of taxa for this site and scenario
PAF - the proportion affect fraction of taxa when exposed to the hazardous concentration of the baseline for that site
chem - chemical abbreviation, the following chemicals (followed by addreviation used) are included:
azinphos-methyl,AZM
carbofuran,CBF
carbaryl,CBL
cadmium,Cd
copper, Cu
DDT,DDT
deltamethrin,DTM
diazinon,DZN
endosulfan,EDS
fenitrothion,FNT
lead,Pb
lindane,LIN
malathion,MLT
methoxychlor,MXC
nickel,Ni
parathion-ethyl,PAE
parathion-methyl,PAM
pentachlorophenol,PCP
zinc, Zn
scenario - the site when under a future (rcpXX, where XX refers to the scenario number of RCP2.6, RCP4.5, RCP6.0 and RCP8.5) scenario
change - direction of change compared to baseline (i.e. PAF > or < 0.05)
moa - mode of action if known
moa2 - mode of action (alternative) if known
type - the type of chemical; either insecticide, metal or biocide
temp - the average temperature change in oC associated with that future scenario
tox_data.zip
A zip folder containing the toxicity data for the 25 chemicals for which toxicity data was finalised.
All files have the same format :
latin - the latin species name (genus and species) for the taxa tested
lconc - the log10 sensitivity of the species to that chemical in ug/L
The following chemicals (followed by addreviation used in file name) are included:
azinphos-methyl,AZM
benzamine,BNZ
carbofuran,CBF
carbaryl,CBL
cadmium,Cd
copper, Cu
DDT,DDT
deltamethrin,DTM
diazinon,DZN
endrin,EDR
endosulfan,EDS
fluoranthene,FLN
fenitrothion,FNT
lead,Pb
lindane,LIN
malathion,MLT
methoxychlor,MXC
nickel,Ni
nonyl-phenol,NPH
parathion-ethyl,PAE
parathion-methyl,PAM
pentachlorophenol,PCP
phenol,PHN
zinc, Zn
01_ukcp18.zip
02_RICT.zip
03_hSSD.zip
Results were gained from running three R projects in sequence, ukcp18, RICT and hSSD.
These have been uploaded here numerically as 01, 02 and 03 respectively for the R scripts and input files necessary to reproduce the results. The R scripts in each zip file are also numerically ordered by the order in which they should be run.
The output files used in the analysis and reporting of this manuscript have been uploaded below the zip files.
By running the input and R zip files for each project in order, the full set of results can be reobtained. Note that as the Bayesian MCMC sample runs are random, repeated results may differ slightly from those in the manuscript.
Access Information
Data were adapted or derived from the following sources:
UKCP18 - Met Office. (2019). Met Office Hadley Centre (2019): UKCP local projections on a 5 km grid over the UK for 1980–2080. https://catalogue.ceda.ac.uk/uuid/e304987739e
RICT - Environment Agency (EA), NIEA, NRW, and SEPA. (2021). River Invertebrate Classification Tool (RICT). Freshwater Biology Association. https://www.fba.org.uk/FBA/Public/Discover-and-Learn/Projects/RIVPACS_Landing.aspx available at: https://www.fba.org.uk/rivpacs-and-rict/river-invertebrate-classification-tool
hSSD model - Craig, P. S. (2013). Exploring novel ways of using species sensitivity distributions to establish PNECs for industrial chemicals: Final report to project steering group 3 April 2013. Technical report. http://dro.dur.ac.uk/13383/
Taxonomy - Chamberlain, S. A., & Szöcs, E. (2013). Taxize: Taxonomic search and retrieval in R [version 2; peer review: 3 approved]. F1000Research, 2, 191. https://doi.org/10.12688/f1000research.2-191.v2
Schoch, C. L., Ciufo, S., Domrachev, M., Hotton, C. L., Kannan, S., Khovanskaya, R., & Leipe, D. (2020). NCBI taxonomy: A comprehensive update on curation resources and tools. Database. https://doi.org/10.1093/database/baaa062
Research Domain: 1.6 Biological sciences