Conventional cladistic methods of inferring evolutionary relationships exclude temporal data from the initial search for optimal hypotheses, but stratocladistics includes such data. A comparison of the ability of these methods to recover known, simulated evolutionary histories given the same, evolved character data shows that stratocladistics recovers the true phylogeny in over twice as many cases as cladistics (42 versus 18 percent). The comparison involved 550 unique taxon-by-character matrices, representing 15 evolutionary models and fossil records ranging from 100 to 10 percent complete.

Accurate estimates of predation intensity, the frequency of mortality from predation, are critical to studies of the evolution of species in response to predation, and to studies of predator-prey systems in general. Most commonly used indirect proxies for predation intensity in the fossil record have logistical or theoretical problems. Direct proxies, using actual traces of predatory activity, such as drilling and repair scars, may hold more promise. However, these direct proxies often have been used in conjunction with optimal foraging models, and in this context, the underlying assumptions and predictions of optimal foraging are examined.Results from theoretical models using optimal foraging suggest that (1) the ratio of internal shell volume to shell thickness of prey (benefit/cost ratio) may be a questionable measurement of prey “value” to the predator, as it fails to account adequately for energetic cost to the predator during pursuit and grappling; (2) drilling and repair frequency are invalid measures of prey preference, because optimal foraging predicts that specific prey types are either always taken or always ignored; (3) pooled drilling frequency will not be a useful metric of predation intensity in systems in which the predator need not always drill; and (4) an increase in repair frequency can be a consequence of either an increase or a decrease in predation intensity.Although drilling frequency may not indicate prey preference, it is a valid proxy for selection due to predation. An approach using size classes, in which the minimum size at which a predation refuge is achieved, is suggested for use with repair frequency.

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Community ecologists commonly perform multivariate techniques (e.g., ordination, cluster analysis) to assess patterns and gradients of taxonomic variation. A critical requirement for a meaningful statistical analysis is accurate information on the taxa found within an ecological sample. However, oversampling (too many individuals counted per sample) also comes at a cost, particularly for ecological systems in which identification and quantification is substantially more resource consuming than the field expedition itself. In such systems, an increasingly larger sample size will eventually result in diminishing returns in improving any pattern or gradient revealed by the data, but will also lead to continually increasing costs. Here, we examine 396 datasets: 44 previously published and 352 created datasets. Using meta-analytic and simulation-based approaches, the research within the present paper seeks (1) to determine minimal sample sizes required to produce robust multivariate statistical results when conducting abundance-based, community ecology research. Furthermore, we seek (2) to determine the dataset parameters (i.e., evenness, number of taxa, number of samples) that require larger sample sizes, regardless of resource availability. We found that in the 44 previously published and the 220 created datasets with randomly chosen abundances, a conservative estimate of a sample size of 58 produced the same multivariate results as all larger sample sizes. However, this minimal number varies as a function of evenness, where increased evenness resulted in increased minimal sample sizes. Sample sizes as small as 58 individuals are sufficient for a broad range of multivariate abundance-based research. In cases when resource availability is the limiting factor for conducting a project (e.g., small university, time to conduct the research project), statistically viable results can still be obtained with less of an investment.

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