10.5061/DRYAD.DV41NS1TR
Reverter, Miriam
0000-0002-7743-8647
Carl von Ossietzky University of Oldenburg
Sarter, Samira
0000-0001-5115-0824
Centre de Coopération Internationale en Recherche Agronomique pour le
Développement
Caruso, Domenico
Institut de Recherche pour le Développement
Avarre, Jean-Christophe
0000-0001-6899-7052
Institut de Recherche pour le Développement
Combe, Marine
Institut de Recherche pour le Développement
Pepey, Elodie
0000-0003-2624-0929
Centre de Coopération Internationale en Recherche Agronomique pour le
Développement
Pouyaud, Laurent
0000-0003-4415-9198
Institut de Recherche pour le Développement
de Verdal, Hugues
0000-0002-1923-8575
Centre de Coopération Internationale en Recherche Agronomique pour le
Développement
Vega-Heredía, Sarahi
Institut de Recherche pour le Développement
Gozlan, Rodolphe
0000-0003-1773-3545
Institut de Recherche pour le Développement
Aquaculture at the crossroads of global warming and antimicrobial resistance
Dryad
dataset
2019
Aquaculture
Bacterial infection
aquatic animal mortality
LMIC countries
climate vulnerability
French National Research Institute for Development (IRD)
2020-03-04T00:00:00Z
2020-03-04T00:00:00Z
en
263069 bytes
5
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
In many developing countries, aquaculture is key to ensuring food security
for millions of people. It is thus important to measure the full
implications of environmental changes on the sustainability of
aquaculture. We conducted a double meta-analysis (460 articles) to explore
how global warming and antimicrobial resistance (AMR) impacts aquaculture.
We calculated a Multi-Antibiotic Resistance index (MAR) of
aquaculture-related bacteria (11,274 strains) for 40 countries, of which
mostly low- and middle-income countries present high AMR levels. Here we
show that aquaculture MAR indices correlate with MAR indices from clinical
bacteria, temperature and countries’ climate vulnerability. We also found
that infected aquatic animals present higher mortalities at warmer
temperatures. Countries most vulnerable to climate change will probably
face the highest AMR risks, impacting human health beyond the aquaculture
sector, highlighting the need for urgent action. Sustainable solutions to
minimize antibiotic use and increase system resilience are therefore
urgently needed.
Data collection Literature research strategy We systematically searched
all peer-reviewed journal articles and theses using Web of Science and
Google scholar up to March 1st, 2019 that investigated 1) mortalities from
cultured aquatic animals due bacterial infections (dataset 1) and 2) AMR
from aquaculture environments (dataset 2). Since AMR changes over time, we
only retained articles on this subject published within the last 10 years.
We performed two independent literature searches for each of the subjects
following the PRISMA (Preferred Reporting Items for Systematic Reviews and
Meta-Analyses) guidelines (Supplementary Figure 2, 3). The following
keyword combinations were used: 1) (aquaculture* OR farm* OR rear*) AND
(fish OR shrimp OR shellfish) AND (mortality OR outbreak OR infection) AND
(Aeromonas OR Edwardsiella OR Flavobacterium OR Streptococc* OR Vibrio OR
Yersinia) and 2) (antimicrobial or antibiotic) AND (resistance OR
susceptibil*) AND (aquaculture OR fish OR shrimp OR shellfish). These
searches produced a total of 3,526 records for the dataset 1 and 4,512
records for the dataset 2 that were filtered in a three-stage process
(Supplementary Figure 1, 2). After removal of duplicates, issued from
combining several database searches, title and abstract of the remaining
records (2,458 for dataset 1 and 2,556 for dataset 2) were scanned for
relevance in the studied topics. Then, the full-text of the retained
articles (837 for dataset 1 and 697 for dataset 2) were assessed.
Inclusion criteria and data extraction Dataset 1: Only research articles
where an experimental infection was performed with a clear identified
protocol were considered. Natural outbreaks were not considered due to the
difficulty of determining 1) whether a previous treatment (e.g. vaccine or
antibiotic) was applied, 2) exact temperature during the duration of the
outbreak and 3) whether the outbreak was uniquely caused by one clearly
identified bacterial pathogen. All selected studies met the following
criteria: 1) experimental infections were performed with pure bacterial
cultures previously characterized, 2) dose of infection and mode of
infection were clearly identified, 3) the life stage of the organism
infected was reported, 4) temperature during the duration of the outbreak
was clearly reported and constant (± 1°C), 5) the animal mortality was
reported as % and 6) aquatic infected animals were not exposed to any
substance or stress that might have interfered with the mortality outcome
When a study included several experiments under different temperatures,
host species or pathogen species, we considered them distinct
observations. Following, all the aforementioned criteria we obtained a
dataset containing 582 observations extracted from 273 studies
(Supplementary Figure 2, 4, Supplementary data 1). For each of the
observations we extracted the following data: pathogen and host taxonomy
(species, family and phylum) host developmental stage (larvae, juvenile,
adult), country, temperature of the infection, cumulative mortality, mode
of infection (injection or immersion) and infective dose. Dataset 2: Only
research articles reporting antimicrobial resistances of bacteria isolated
directly from the aquaculture environment (cultured animals recovered at
the farmed site, water or sediment) were considered. Articles reporting
antimicrobial resistances of isolated bacteria from cultured animals
recovered in any other site than the farming environment, such as retail
markets or imported products, were not included to avoid a bias introduced
by potential contamination during transport. All selected studies met the
following criteria: 1) antimicrobial activity of bacterial strains was
reported for at least 3 antibiotics, 2) at least the bacterial genus was
identified in order to be able to disregard susceptibilities to
antibiotics for which they are naturally resistant (Supplementary Table
20) and 3) bacteria studied were known as pathogenic for aquatic cultured
animals. Since Pseudomonas species are known to present numerous intrinsic
AMR, they were excluded from the analysis in order to avoid biased
results. For the calculation of the countries’ MAR indices, we established
a minimum requirement of 30 bacterial strains. This led to a dataset that
contained antimicrobial resistances of 11,274 strains extracted from 187
studies (Supplementary Figure 5). For each of these studies the following
information was extracted: country of the study, bacterial species or
genus, source of isolation (host species or type of farm), number of
antibiotics tested and number of resistant strains.