We studied the pollination ecology and assemblage structure of 31 species of Stylidium (Stylidiaceae) at 25 sites in Western Australia. The number of species per study site varied between two and size. Stylidium species are pollinated by a variety of nectar—seeking solitary bees and bombyliid flies. Within and among species there is significant variation in nectar—tube length (and therefore in the insects that visit the flowers) and in pollen placement on pollinators. Pollen is placed “explosively” on the insect by a motile column of fused staminate and pistillate tissues; the position and reach of the column varies within and among species, thereby causing variation in site of pollen deposition. When discrete pollination niches were defined for all species, only one niche overlap was observed across the 86 interacting pairs of Stylidium species at the 25 sites. To determine whether this was a nonrandom assemblage structure we compared our observation with the outcome of null models. We developed three null models to cover the most likely structuring processes: that communities are organized by (1) ecological sorting, (2) evolution of plant phenotypes, or (3) both processes. We concluded that it was unlikely (P = .055—.002) that so few overlaps in pollination niches would occur by chance. We developed another null model to test whether chance could have created the apparent pattern of character displacement in pollination niches exhibited by the nine species showing intraspecific variation. The analysis indicated that character displacement has probably occurred (P = .014). This study is one of the clearest demonstrations to date of reproductive interactions generating assemblage structure and character displacement in plants.

Simulations are necessary to assess the performance of home-range estimators because the true distribution of empirical data is unknown, but we must question whether that performance applies to empirical data. Some studies have used empirically based simulations, randomly selecting subsets of data to evaluate estimator performance, but animals do not move randomly within a home range. We created an empirically based simulation using a behavioral model, generated a probability distribution from those data, and randomly selected locations from that distribution in a chronological sequence as the simulated individual moved through its home range. Thus, we examined the influence of temporal patterns of space use and determined the effects of smoothing, number of locations, and autocorrelation on kernel estimates. Additionally, home-range estimators were designed to evaluate species that use space with few restrictions, traveling almost anywhere on the landscape. Many species, however, confine their movements to a geographical feature that conforms to a relatively linear pattern. Consequently, conventional analysis techniques may overestimate home ranges. We used simulations based upon coastal river otters (Lontra canadensis), a species that primarily uses the aquatic-terrestrial interface, to evaluate the efficacy of fixed and adaptive kernel estimates with various smoothing parameters. Measures of shoreline length within contours from fixed kernel analyses and the reference smoothing parameter were best for estimates of 95% home ranges, because smoothing with least squares cross validation (LSCV) often resulted in inconsistent results, excessive fragmentation, and marked underestimates of linear home ranges. Core areas (50% density contours) were best defined with fixed kernel LSCV estimates. Fewer locations underestimated linear home ranges, and there was a subtle positive relation between home-range size and autocorrelation. Generally, as location numbers increased, autocorrelation increased, but differences from the ''true'' home range decreased. Results were similar for our simulations and empirical data from 13 river otters. Examination of empirical data revealed that data with high positive autocorrelation illustrated seasonal reproductive activities. Because autocorrelation does not negatively influence estimates of linear home ranges, assessment of independence between data points may be more appropriately viewed as a means to identify important behavioral information, rather than as a hindrance.

The repeated evolution of fused carpels (syncarpy) is one of the dominant features of angiosperm macroevolution. We present results of new phylogenetic and theoretical analyses to assess the frequency and nature of transitions to syncarpy, and the possible advantages of syncarpy over apocarpy under a variety of ecological conditions. Using a recent molecular estimate of angiosperm phylogeny, we ascertained that a minimum of 17 independent evolutionary transitions from apocarpy to syncarpy have occurred; about three‐quarters of these transitions allowed pollen tubes to cross between carpels and fertilize ovules that would otherwise be left unfertilized. Most of these transitions also intensified competition between pollen, potentially enhancing offspring fitness. The high proportion of evolutionary transitions promoting pollen competition and pollen‐tube access to all carpels supports the hypothesis that the main advantage of syncarpy is in increasing offspring quality and quantity. The potential advantages of syncarpy were more thoroughly evaluated by analytical and simulation studies. These showed that the advantage of syncarpy over apocarpy involving increased offspring‐quantity held under conditions of marginal pollination and declined with increasing pollination. The offspring‐quality advantage persisted over a wider range of conditions, including under quite high pollination rates.

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Simulations are necessary to assess the performance of home‐range estimators because the true distribution of empirical data is unknown, but we must question whether that performance applies to empirical data. Some studies have used empirically based simulations, randomly selecting subsets of data to evaluate estimator performance, but animals do not move randomly within a home range. We created an empirically based simulation using a behavioral model, generated a probability distribution from those data, and randomly selected locations from that distribution in a chronological sequence as the simulated individual moved through its home range. Thus, we examined the influence of temporal patterns of space use and determined the effects of smoothing, number of locations, and autocorrelation on kernel estimates. Additionally, home‐range estimators were designed to evaluate species that use space with few restrictions, traveling almost anywhere on the landscape. Many species, however, confine their movements to a geographical feature that conforms to a relatively linear pattern. Consequently, conventional analysis techniques may overestimate home ranges. We used simulations based upon coastal river otters (Lontra canadensis), a species that primarily uses the aquatic–terrestrial interface, to evaluate the efficacy of fixed and adaptive kernel estimates with various smoothing parameters. Measures of shoreline length within contours from fixed kernel analyses and the reference smoothing parameter were best for estimates of 95% home ranges, because smoothing with least squares cross validation (LSCV) often resulted in inconsistent results, excessive fragmentation, and marked underestimates of linear home ranges. Core areas (50% density contours) were best defined with fixed kernel LSCV estimates. Fewer locations underestimated linear home ranges, and there was a subtle positive relation between home‐range size and autocorrelation. Generally, as location numbers increased, autocorrelation increased, but differences from the “true” home range decreased. Results were similar for our simulations and empirical data from 13 river otters. Examination of empirical data revealed that data with high positive autocorrelation illustrated seasonal reproductive activities. Because autocorrelation does not negatively influence estimates of linear home ranges, assessment of independence between data points may be more appropriately viewed as a means to identify important behavioral information, rather than as a hindrance.