资源环境科技发展态势分析平台

Variation in vegetation and climate affects the long-term changes in bird communities in intensive-agriculture habitats, but not in diversified-agriculture or natural-forest habitats, by changing the local colonization and extinction rates.

Agricultural practices constitute both the greatest cause of biodiversity loss and the greatest opportunity for conservation(1,2), given the shrinking scope of protected areas in many regions. Recent studies have documented the high levels of biodiversity-across many taxa and biomes-that agricultural landscapes can support over the short term(1,3,4). However, little is known about the long-term effects of alternative agricultural practices on ecological communities(4,5) Here we document changes in bird communities in intensive-agriculture, diversified-agriculture and natural-forest habitats in 4 regions of Costa Rica over a period of 18 years. Long-term directional shifts in bird communities were evident in intensive- and diversified-agricultural habitats, but were strongest in intensive-agricultural habitats, where the number of endemic and International Union for Conservation of Nature (IUCN) Red List species fell over time. All major guilds, including those involved in pest control, pollination and seed dispersal, were affected. Bird communities in intensive-agricultural habitats proved more susceptible to changes in climate, with hotter and drier periods associated with greater changes in community composition in these settings. These findings demonstrate that diversified agriculture can help to alleviate the long-term loss of biodiversity outside natural protected areas(1).

Since its introduction, the reward prediction error theory of dopamine has explained a wealth of empirical phenomena, providing a unifying framework for understanding the representation of reward and value in the brain(1-3). According to the now canonical theory, reward predictions are represented as a single scalar quantity, which supports learning about the expectation, or mean, of stochastic outcomes. Here we propose an account of dopamine-based reinforcement learning inspired by recent artificial intelligence research on distributional reinforcement learning(4-6). We hypothesized that the brain represents possible future rewards not as a single mean, but instead as a probability distribution, effectively representing multiple future outcomes simultaneously and in parallel. This idea implies a set of empirical predictions, which we tested using single-unit recordings from mouse ventral tegmental area. Our findings provide strong evidence for a neural realization of distributional reinforcement learning.

Analyses of single-cell recordings from mouse ventral tegmental area are consistent with a model of reinforcement learning in which the brain represents possible future rewards not as a single mean of stochastic outcomes, as in the canonical model, but instead as a probability distribution.