Menezes, M. | Search bioRxiv

bioRxiv2025

Ecological processes shaping marine microbial assemblages diverge between equatorial and temperate time-series

Marine microbial communities are structured by a complex interplay of deterministic and stochastic processes, yet how these vary across latitudes remains poorly understood. Most long-term microbial observatories are restricted to temperate regions, limiting our ability to assess latitudinal contrasts in microbial dynamics. Here, we compare coastal microbial communities from two contrasting marine time-series stations using standardized molecular protocols: a new tropical site in the Equatorial Atlantic (EAMO, 6{degrees}S) and a well-studied temperate site in the Mediterranean Sea (BBMO, 41{degrees}N). Monthly 16S and 18S rRNA gene sequencing of two size-fractions (0.22-3 {micro}m and >3 {micro}m) over 41 months (from April 2013 to August 2016) revealed marked differences in taxonomic composition, temporal variability, and ecological assembly processes. Temperate communities exhibited strong seasonal turnover, higher beta-diversity, and tighter coupling with environmental variables such as temperature and daylength. In contrast, tropical communities were compositionally more stable and more governed by biotic factors and stochastic processes such as historical contingency and ecological drift. These patterns were consistent across taxonomic domains and size-fractions, though selection was generally stronger in prokaryotes and the smallest size-fraction. Co-occurrence networks at the temperate site were more densely connected and environmentally responsive compared to tropical networks, where stochastic processes and putative biological interactions gain prominence. This study highlights the importance of integrating observatories from underrepresented latitudes into global microbial monitoring efforts, particularly as climate change alters the amplitude and frequency of environmental drivers across the ocean.

ecology↗

bioRxiv2025

Revealing the mechanisms underlying latent learning with successor representations

Latent learning experiments were critical in shaping Tolmans cognitive map theory. In a spatial navigation task, latent learning means that animals acquire knowledge of their environment through exploration, such that pre-exposed animals learn faster on a subsequent learning task than naive ones. This enhancement has been shown to depend on the design of the pre-exposure phase. Here, we hypothesize that the deep successor representation (DSR), a recent computational model for cognitive map formation, can account for the modulation of latent learning because it is sensitive to the statistics of behavior during exploration. In our model, exploration aligned with the future reward location significantly improves reward learning compared to random, misdirected, or no exploration, as reported by experiments. This effect generalizes across different action selection strategies. We show that these performance differences follow from the spatial information encoded in the structure of the DSR acquired in the pre-exposure phase. In summary, this study sheds light on the mechanisms underlying latent learning and how such learning shapes cognitive maps, impacting their effectiveness in goal-directed spatial tasks. Author summaryLatent learning enables animals to construct cognitive maps of their environment without direct reinforcement. This process facilitates efficient navigation when rewards are introduced later, that is, animals familiar with a maze through prior exposure learn rewarded tasks faster than those without pre-exposure. Evidence suggests that the design of the pre-exposure phase significantly impacts the effectiveness of latent learning. Targeted pre-exposure focused on future reward locations enhances learning more than generic pre-exposure. However, the underlying mechanisms driving these differences remain understudied. This study investigates how pre-exposure methods influence subsequent navigation task performance using an artificial agent based on deep successor representations -- a model for learning cognitive maps -- within a reinforcement learning framework. Our findings reveal that before reward learning, agents receiving targeted pre-exposure develop spatial features more closely aligned with those of agents learning from rewards, compared to agents experiencing generic pre-exposure. This alignment enables the targeted pre-exposure agent to take more effective targeted actions, resulting in accelerated initial learning. The persistence of this advantage, even when modifying the agents exploration policy, indicates a robust cognitive map within the successor representation.

neuroscience↗