Seasonal variation and an “outbreak” of frog predation by tamarins

We report temporal variation and an “outbreak” of frog predation by moustached tamarins, Saguinus mystax, in north-eastern Peruvian Amazonia. Frog predation rates were generally very low, but strongly increased in October 2015. Other high rates, identified by outlier analyses, were also observed in September–November of other years. Over all study years, predation rates in this 3-month period were significantly higher than those in the remainder of the year, suggesting a seasonal pattern of frog predation by tamarins. Reduced fruit availability or increased frog abundance or a combination of both may be responsible for both the seasonal pattern and the specific “outbreak” of frog predation

On the mating system of the cooperatively breeding saddle-backed tamarin ( Saguinus fuscicollis )

This paper reports on 5 years of observatiors of individually marked saddle-backed tamarins ( Saguinus fuscicollis , Callitrichidae). Although callitrichids have long been presumed to have a monogamous social system, this study shows that the breeding structure of saddle-back tamarin groups is highly variable. Groups most commonly include two or more adult males and a single reproductive female, but occasionally contain only a single pair of adults, or less often, two reproductively active females and one or more males. Data on group compositions, group formations, intergroup movements and copulations show that the social and mating systems of this species are more flexible than those of any other non-human primate yet studied. Infants (usually twins) were cared for by all group members. There were two classes of helpers: young, nonreproductive individuals who helped to care for full or half siblings, and cooperatively polyandrous males who cared for infants whom they may have fathered. The observations suggest that non-reproductive helpers may benefit from their helping behavior through a combination of inclusive fitness gains, reciprocal altruism, and the value of gaining experience at parental care.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46874/1/265_2004_Article_BF00295541.pd

Dynamic Treatment Regimes

In recent years, treatment and intervention scientists increasingly realize that individual heterogeneity in disorder severity, background characteristics and co-occurring problems translates into heterogeneity in response to various aspects of any treatment program. Accordingly, research in this area is shifting from the traditional “one-size-fits-all ” treatment to dynamic treatment regimes, which allow greater individualization in programming over time. A dynamic treatment regime is a sequence of decision rules that specify how the dosage and/or type of treatment should be adjusted through time in response to an individual’s changing needs, aiming to optimize the effectiveness of the program. In the chapter we review the Sequential Multiple Assignment Randomized Trials (SMART), which is an experimental design useful for the development of dynamic treatment regimes. We compare the SMART approach with other experimental approaches and discuss data analyses methods for constructing a high quality dynamic treatment regime as well as other secondary analyses. 1

A Reckless Guide to P-values : Local Evidence, Global Errors.

This chapter demystifies P-values, hypothesis tests and significance tests and introduces the concepts of local evidence and global error rates. The local evidence is embodied in this data and concerns the hypotheses of interest for this experiment, whereas the global error rate is a property of the statistical analysis and sampling procedure. It is shown using simple examples that local evidence and global error rates can be, and should be, considered together when making inferences. Power analysis for experimental design for hypothesis testing is explained, along with the more locally focussed expected P-values. Issues relating to multiple testing, HARKing and P-hacking are explained, and it is shown that, in many situations, their effects on local evidence and global error rates are in conflict, a conflict that can always be overcome by a fresh dataset from replication of key experiments. Statistics is complicated, and so is science. There is no singular right way to do either, and universally acceptable compromises may not exist. Statistics offers a wide array of tools for assisting with scientific inference by calibrating uncertainty, but statistical inference is not a substitute for scientific inference. P-values are useful indices of evidence and deserve their place in the statistical toolbox of basic pharmacologists