The biogeography of South African terrestrial plant invasions

Thousands of plant species have been introduced, intentionally and accidentally, to South Africa from many parts of the world. Alien plants are now conspicuous features of many South African landscapes and hundreds of species have naturalised (i.e. reproduce regularly without human intervention), many of which are also invasive (i.e. have spread over long distances). There is no comprehensive inventory of alien, naturalised, and invasive plants for South Africa, but 327 plant taxa, most of which are invasive, are listed in national legislation. We collated records of 759 plant taxa in 126 families and 418 genera that have naturalised in natural and semi-natural ecosystems. Over half of these naturalised taxa are trees or shrubs, just under a tenth are in the families Fabaceae (73 taxa) and Asteraceae (64); genera with the most species are Eucalyptus,Acacia, and Opuntia. The southern African Plant Invaders Atlas (SAPIA) provides the best data for assessing the extent of invasions at the national scale. SAPIA data show that naturalised plants occur in 83% of quarter-degree grid cells in the country. While SAPIA data highlight general distribution patterns (high alien plant species richness in areas with high native plant species richness and around the main human settlements), an accurate, repeatable method for estimating the area invaded by plants is lacking. Introductions and dissemination of alien plants over more than three centuries, and invasions over at least 120 years (and especially in the last 50 years) have shaped the distribution of alien plants in South Africa. Distribution patterns of naturalised and invasive plants define four ecologically-meaningful clusters or “alien plant species assemblage zones”, each with signature alien plant taxa for which trait-environment interactions can be postulated as strong determinants of success. Some widespread invasive taxa occur in high frequencies across multiple zones; these taxa occur mainly in riparian zones and other azonal habitats,or depend on human-mediated disturbance, which weakens or overcomes the factors that determine specificity to any biogeographical region

Recognizing sequences of sequences

The brain's decoding of fast sensory streams is currently impossible to emulate, even approximately, with artificial agents. For example, robust speech recognition is relatively easy for humans but exceptionally difficult for artificial speech recognition systems. In this paper, we propose that recognition can be simplified with an internal model of how sensory input is generated, when formulated in a Bayesian framework. We show that a plausible candidate for an internal or generative model is a hierarchy of 'stable heteroclinic channels'. This model describes continuous dynamics in the environment as a hierarchy of sequences, where slower sequences cause faster sequences. Under this model, online recognition corresponds to the dynamic decoding of causal sequences, giving a representation of the environment with predictive power on several timescales. We illustrate the ensuing decoding or recognition scheme using synthetic sequences of syllables, where syllables are sequences of phonemes and phonemes are sequences of sound-wave modulations. By presenting anomalous stimuli, we find that the resulting recognition dynamics disclose inference at multiple time scales and are reminiscent of neuronal dynamics seen in the real brain