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The reproductive biology of the dioecious understorey palm Chamaedorea radicalis was investigated in order to identify the primary pollen vector and quantify the relationship between female fecundity and local neighbourhood sexual composition. The study was conducted in a montane mesophyll forest within the El Cielo Biosphere Reserve, Tamaulipas, Mexico. The species is considered vulnerable in Mexico and there are concerns about the sustainability of leaf harvest. We determined that wind is the primary pollen vector, based both on floral and pollen morphological characters, and on a pollinator exclosure experiment. Successful wind pollination of this understorey palm was facilitated by the extended flowering period of males, which allows one male to be a source of pollen to receptive females for as long as a month. The number of flowers and fruits borne on a female were dependent on female size, however, no size parameter correlated well with fruit set. Fruit set was also not dependent on local sexual composition, male density or distance to the nearest male, suggesting that in this study area female reproductive success is not limited by the availability of pollen.

Experimental time series provide an informative window into the underlying dynamical system, and the timing of the extrema of a time series (or its derivative) contains information about its structure. However, the time series often contain significant measurement errors. We describe a method for characterizing a time series for any assumed level of measurement error ε by a sequence of intervals, each of which is guaranteed to contain an extremum for any function that ε-approximates the time series. Based on the merge tree of a continuous function, we define a new object called the normalized branch decomposition, which allows us to compute intervals for any level ε.We show that there is a well-defined total order on these intervals for a single time series, and that it is naturally extended to a partial order across a collection of time series comprising a dataset. We use the order of the extracted intervals in two applications. First, the partial order describing a single dataset can be used to pattern match against switching model output [1], which allows the rejection of a network model. Second, the comparison between graph distances of the partial orders of different datasets can be used to quantify similarity between biological replicates.