Rasconi, S. | bioRxiv Science

bioRxiv2022

Chytrids-conveyed long-chain polyunsaturated fatty acids to Daphnia alleviate the detrimental effect of heat when combined with limiting dietary organic matter quantity and nutritional quality

Global warming enhances the dominance of poorly palatable PUFA-deprived bloom-forming cyanobacteria. Chytrid fungal parasites increase herbivory and dietary access to polyunsaturated fatty acids (PUFA) across the phytoplankton-zooplankton interface. Little is known however about the role chytrids may play in compensating for the decrease of algae-derived PUFA under global warming scenarios. We tested experimentally the combined effects of water temperature increase and the presence of chytrids with Daphnia magna as the consumer and the cyanobacterium Planktothrix rubescens as the main diet. We hypothesised that the diet including chytrids would enhance Daphnia fitness due to increased PUFA transfer irrespective of water temperature. Chytrid-infected diet significantly increased Daphnia survival, somatic growth, and reproduction, irrespective of water temperature. The PUFA content of Daphnia feeding on the chytrid-infected diet was unaffected by heat at the onset of the first successful reproduction. Carbon stable isotopes of fatty acids highlighted preferential n-3 PUFA upgrading by chytrids and an ~3x higher endogenous n-3 PUFA conversion compared with n-6 PUFA by Daphnia, irrespective of water temperature. Diet including chytrids enhanced the retention of eicosapentaenoic acid (EPA; 20:5n-3) and arachidonic acid (ARA; 20:4n-6) in Daphnia. The heat did not decrease EPA and even increased ARA retention by enhanced endogenous bioconversion in Daphnia when feeding on the chytrid-infected diet. We conclude that chytrids support Daphnia fitness at higher water temperatures via increased n-3 and n-6 PUFA retention and preferential n-3 PUFA bioconversion. Thus, they help function pelagic ecosystems with PUFA availability at the phytoplankton-zooplankton interface in a warmer climate.

ecology↗

bioRxiv2021

Neural networks and extreme gradient boosting predict multiple thresholds and trajectories of microbial biodiversity responses due to browning

Ecological association studies often assume monotonicity such as between biodiversity and environmental properties although there is growing evidence that non-monotonic relations dominate in nature. Here we apply machine learning algorithms to reveal the non-monotonic association between microbial diversity and an anthropogenic induced large scale change, the browning of freshwaters, along a longitudinal gradient covering 70 boreal lakes in Scandinavia. Measures of bacterial richness and evenness (alpha diversity) showed non-monotonic trends in relation to environmental gradients, peaking at intermediate levels of browning. Depending on the statistical methods, variables indicative for browning could explain 5% of the variance in bacterial community composition (beta diversity) when applying standard methods assuming monotonic relations and up to 45 % with machine learning methods (i.e. extreme gradient boosting and feed-forward neural networks) taking non-monotonicity into account. This non-monotonicity observed at the community level was explained by the complex interchangeable nature of individual taxa responses as shown by a high degree of non-monotonic responses of individual bacterial sequence variants to browning. Furthermore, the non-monotonic models provide the position of thresholds and predict alternative bacterial diversity trajectories in boreal freshwater as a result of ongoing climate and land use changes, which in turn will affect entire ecosystem metabolism and likely greenhouse gas production.

microbiology↗