Aquaculture at the crossroads of global warming and antimicrobial resistance

Abstract

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 conduct a double meta-analysis (460 articles) to explore how global warming and antimicrobial resistance (AMR) impact aquaculture. We calculate a Multi-Antibiotic Resistance index (MAR) of aquaculture-related bacteria (11,274 isolates) 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 human clinical bacteria, temperature and countries' climate vulnerability. We also find 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 minimise antibiotic use and increase system resilience are therefore needed.

Fig. 1. Predicted changes in mortality (%) of reared aquatic animals infected by bacterial diseases in response to temperature (°C).

Bacterial pathogens: Aeromonas spp., Edwardsiella spp., F. columnare, Lactococcus spp., Streptococcus spp., Vibrio spp., and Yersinia spp. Red indicates tropical and subtropical host species (n = 329), blue indicates temperate host species (n = 129). Dots represent the raw data and the lines the linear mixed model predictions with SE.

Fig. 2. Global multi-antibiotic resistance (MAR) index calculated from aquaculture-derived bacteria.

No MAR index was calculated for countries in white due to data deficiency.

Fig. 3. Correlations between MAR calculated from aquaculture-related bacteria and human clinical bacteria, temperature, and countries’ climate vulnerability.

Pearson correlations (two-sided test) a MAR from human clinical bacteria (n = 29, P-value < 0.001), b HSBC climate vulnerability index (n = 32, P-value = 0.020) and c temperature (n = 40, P-value 0.10). Bubbles sizes are proportional to national aquaculture production standardised by the total country human population. The colours indicate different Worldbank categories (High income, Upper-middle income and Low-middle income). 1: Vietnam, 2: India, 3: Pakistan, 4: Bangladesh displayed simultaneously the highest levels of clinical and aquaculture MAR and are among the ones exposed to the highest climatic vulnerability and temperatures rises. See the Methods section for details.

Fig. 4. Pearson correlation network between all the simple studied variables.

Significant correlations (P-value < 0.05) are displayed with solid lines, whereas correlations (r > 0.30) nearing statistical significance (0.10 > P-value > 0.05) are shown in dashed lines. Edge weight is proportional to the correlation coefficient (r), with line width increasing with higher correlation values.