The evolution of multiple mating: Costs and benefits of polyandry to females and of polygyny to males

Polyandry is a paradox: why do females mate multiple times when a single ejaculate often provides enough sperm for lifetime egg production? Gowaty et al. addressed explanations for polyandry in Drosophila pseudoobscura from the perspective of hypotheses based on sex differences in costs of reproduction (CoR). Contrary to CoR, Gowaty et al. showed that (1) a single ejaculate was inadequate for lifetime egg production; (2) polyandry provided fitness benefits to females beyond provision of adequate sperm and (3) fitness benefits of polyandry were not offset by costs. Here, I discuss predictions of the ad hoc hypotheses of CoR and three alternative hypotheses to CoR to facilitate a discussion and further development of a strong inference approach to experiments on the adaptive significance of polyandry for females. Each of the hypotheses makes testable predictions; simultaneous tests of the predictions will provide a strong inference approach to understanding the adaptive significance of multiple mating. I describe a sex-symmetric experiment meant to evaluate variation in fitness among lifelong virgins (V); monogamous females and males with one copulation (M_OC); monogamous females and males with multiple copulations (M_MC); P_AND, polyandrous females; and P_GYN, polygynous males. Last, I recommend the study of many different species, while taking care in choice of study species and attention to the assumptions of specific hypotheses. I particularly urge the study of many more Drosophila species both in laboratory and the wild to understand the “nature of flies in nature,” where opportunities and constraints mold evolutionary responses.     

Inference on Multiple Endpoints in Clinical Trials with Adaptive Interim Analyses

Planned interim analyses which permit early stopping or sample size adaption of a trial are desirable for ethical and scientific reasons. Multiple test procedures allow inference about several hypotheses within a single clinical trial. In this paper, a method which combines multiple testing with adaptive interim analyses whilst controlling the experimentwise error rate is proposed. The general closed testing principle, the situation of a priori ordered hypotheses, and application of the Bonferroni-Holm method are considered. The practical application of the method is demonstrated by an example.     

Statistical Versus Biological Hypothesis Testing: Response to Steidl

ABSTRACT In spite of the wide use and acceptance of information theoretic approaches in the wildlife sciences, debate continues on the correct use and interpretation of Akaike's Information Criterion as compared to frequentist methods. Misunderstandings as to the fundamental nature of such comparisons continue. Here we agree with Steidl's argument about situation-specific use of each approach. However, Steidl did not make clear the distinction between statistical and biological hypotheses. Certainly model selection is not statistical, or null, hypothesis testing; importantly, it represents a more effective means to test among competing biological, or research, hypotheses. Employed correctly, it leads to superior strength of inference and reduces the risk that favorite hypotheses are uncritically accepted.     

Statistical phylogeography: methods of evaluating and minimizing inference errors

Nested clade phylogeographical analysis (NCPA) has become a common tool in intraspecific phylogeography. To evaluate the validity of its inferences, NCPA was applied to actual data sets with 150 strong a priori expectations, the majority of which had not been analysed previously by NCPA. NCPA did well overall, but it sometimes failed to detect an expected event and less commonly resulted in a false positive. An examination of these errors suggested some alterations in the NCPA inference key, and these modifications reduce the incidence of false positives at the cost of a slight reduction in power. Moreover, NCPA does equally well in inferring events regardless of the presence or absence of other, unrelated events. A reanalysis of some recent computer simulations that are seemingly discordant with these results revealed that NCPA performed appropriately in these simulated samples and was not prone to a high rate of false positives under sampling assumptions that typify real data sets. NCPA makes a posteriori use of an explicit inference key for biological interpretation after statistical hypothesis testing. Alternatives to NCPA that claim that biological inference emerges directly from statistical testing are shown in fact to use an a priori inference key, albeit implicitly. It is argued that the a priori and a posteriori approaches to intraspecific phylogeography are complementary, not contradictory. Finally, cross-validation using multiple DNA regions is shown to be a powerful method of minimizing inference errors. A likelihood ratio hypothesis testing framework has been developed that allows testing of phylogeographical hypotheses, extends NCPA to testing specific hypotheses not within the formal inference key (such as the out-of-Africa replacement hypothesis of recent human evolution) and integrates intra- and interspecific phylogeographical inference.     

Bayesian inference in ecology

Bayesian inference is an important statistical tool that is increasingly being used by ecologists. In a Bayesian analysis, information available before a study is conducted is summarized in a quantitative model or hypothesis: the prior probability distribution. Bayes’ Theorem uses the prior probability distribution and the likelihood of the data to generate a posterior probability distribution. Posterior probability distributions are an epistemological alternative to P‐values and provide a direct measure of the degree of belief that can be placed on models, hypotheses, or parameter estimates. Moreover, Bayesian information‐theoretic methods provide robust measures of the probability of alternative models, and multiple models can be averaged into a single model that reflects uncertainty in model construction and selection. These methods are demonstrated through a simple worked example. Ecologists are using Bayesian inference in studies that range from predicting single‐species population dynamics to understanding ecosystem processes. Not all ecologists, however, appreciate the philosophical underpinnings of Bayesian inference. In particular, Bayesians and frequentists differ in their definition of probability and in their treatment of model parameters as random variables or estimates of true values. These assumptions must be addressed explicitly before deciding whether or not to use Bayesian methods to analyse ecological data.     

The ‘requirement of total evidence’ and its role in phylogenetic systematics

The question of whether or not to partition data for the purposes of inferring phylogenetic hypotheses remains controversial. Opinions have been especially divided since Kluge's (1989, Systematic Zoology 38, 7–25) claim that data partitioning violates the requirement of total evidence (RTE). Unfortunately, advocacy for or against the RTE has not been based on accurate portrayals of the requirement. The RTE is a basic maxim for non-deductive inference, stipulating that evidence must be considered if it has relevance to an inference. Evidence is relevant if it has a positive or negative effect on a given conclusion. In the case of ℈partitioned’ phylogenetic inferences, the RTE is violated, and the basis for rational belief in any conclusion is compromised, unless it is shown that the partitions are evidentially irrelevant to one another. The goal of phylogenetic systematics is to hypothesize past causal conditions to account for observed shared similarities among two or more species. Such inferences are non-deductive, necessitating consideration of the RTE. Some phylogeneticists claim the parsimony criterion as justification for the RTE. There is no relation between the two – parsimony is a relation between a hypothesis and causal question(s). Parsimony does not dictate the content of premises prior to an inference. ℈Taxonomic congruence,’ ℈supertrees,’ and ℈conditional combination’ methods violate the RTE. Taxonomic congruence and supertree methods also fail to achieve the intended goal of phylogenetic inference, such that ℈consensus trees’ and ℈supertrees’ lack an empirical basis. ℈Conditional combination’ is problematic because hypotheses derived from partitioned data cannot be compared – a causal hypothesis inferred to account for a set of effects only has relevance to those effects, not any comparative relevance to other causal hypotheses. A similar problem arises in the comparisons of hypotheses derived from different causal theories.     

Forward and Backward Inference in Spatial Cognition

This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of ‘lower-level’ computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.     

Integrating genomic and phenotypic data to evaluate alternative phylogenetic and species delimitation hypotheses in a recent evolutionary radiation of grasshoppers

Although resolving phylogenetic relationships and establishing species limits are primary goals of systematics, these tasks remain challenging at both conceptual and analytical levels. Here, we integrated genomic and phenotypic data and employed a comprehensive suite of coalescent‐based analyses to develop and evaluate competing phylogenetic and species delimitation hypotheses in a recent evolutionary radiation of grasshoppers (Chorthippus binotatus group) composed of two species and eight putative subspecies. To resolve the evolutionary relationships within this complex, we first evaluated alternative phylogenetic hypotheses arising from multiple schemes of genomic data processing and contrasted genetic‐based inferences with different sources of phenotypic information. Second, we examined the importance of number of loci, demographic priors, number and kind of phenotypic characters and sex‐based trait variation for developing alternative species delimitation hypotheses. The best‐supported topology was largely compatible with phenotypic data and showed the presence of two clades corresponding to the nominative species groups, one including three well‐resolved lineages and the other comprising a four‐lineage polytomy and a well‐differentiated sister taxon. Integrative species delimitation analyses indicated that the number of employed loci had little impact on the obtained inferences but revealed the higher power provided by an increasing number of phenotypic characters and the usefulness of assessing their phylogenetic information content and differences between sexes in among‐taxa trait variation. Overall, our study highlights the importance of integrating multiple sources of information to test competing phylogenetic hypotheses and elucidate the evolutionary history of species complexes representing early stages of divergence where conflicting inferences are more prone to appear.     

msABC: a modification of Hudson's ms to facilitate multi-locus ABC analysis

With the availability of whole-genome sequence data biologists are able to test hypotheses regarding the demography of populations. Furthermore, the advancement of the Approximate Bayesian Computation (ABC) methodology allows the demographic inference to be performed in a simple framework using summary statistics. We present here msABC, a coalescent-based software that facilitates the simulation of multi-locus data, suitable for an ABC analysis. msABC is based on Hudson's ms algorithm, which is used extensively for simulating neutral demographic histories of populations. The flexibility of the original algorithm has been extended so that sample size may vary among loci, missing data can be incorporated in simulations and calculations, and a multitude of summary statistics for single or multiple populations is generated. The source code of msABC is available at http://bio.lmu.de/~pavlidis/msabc or upon request from the authors.     

Testing the Adaptive Significance of Acclimation: A Strong Inference Approach

Acclimation is a common phenotypic response to environmentalchange. Acclimation is often thought to enhance performanceand thus to be adaptive. This view has recently been formalizedas the "Beneficial Acclimation Hypothesis" and predicts thatindividuals acclimated to one environment perform better inthat environment than do individuals acclimated to a differentenvironment. Although Beneficial Acclimation is appealing andwidely supported, recent studies with E. coli and Drosophilahave challenged its general validity. Although these challengescould be dismissed as mere exceptions, they encourage a re-evaluationof the adaptive significance of acclimation. Our philosophicalapproach differs from that of most previous studies of acclimation,in which the prediction derived from a Beneficial Acclimationperspective (e.g., heat tolerance is positively correlated withacclimation temperature) is tested against the null hypothesis("single hypothesis approach"). Instead, we follow Huey andBerrigan (1996) in advocating a strong inference approach (sensuPlatt, 1964), which recognizes that Beneficial Acclimation isactually one of a set of competing hypotheses that make differentpredictions as to how developmental temperature influences thethermal sensitivity of adults ("developmental acclimation").Using this perspective, Huey and Berrigan proposed a factorialexperimental design (3 developmental by 3 adult temperatures)designed to discriminate among all competing hypotheses. Wenow derive a formal statistical model (ANOVA with orthogonalpolynomial contrasts) for this experimental design and use itto evaluate simultaneously the relative impact of each competinghypothesis. We then apply this model to several case studies(Drosophila, Volvox, Trichogramma), and we review also a recentstudy with E. coli. The influence of Beneficial Acclimationis supported (albeit often weakly) in most cases. Nevertheless,other hypotheses (especially the Optimal Developmental TemperatureHypothesis) often have a greater impact. Even so, however, BeneficialAcclimation usually predicts relative performance at extremetest temperatures. We conclude that, although rumors of itsdeath are premature, Beneficial Acclimation cannot be viewedas the dominant expectation, at least with regard to developmentaltemperature acclimation. Moreover, our findings reinforce theview that a strong inference approach provides a more comprehensiveportrait of complex biological responses than do single-hypothesisapproaches.     

Alternative statistical approaches to the use of data as evidence for hypotheses in human behavioral ecology

In their ambitious Evolutionary Anthropology paper, Winterhalder and Smith 1 review the history, theory, and methods of human behavioral ecology (HBE). In establishing how HBE differs from traditional approaches within sociocultural anthropology, they and others laud its hypothetical‐deductive research method. 1 - 3 Our aim is to critically examine how human behavioral ecologists conduct their research, specifically how they analyze and interpret data as evidence for scientific hypotheses. Through computer simulations and a review of empirical studies of human sex ratios, we consider some limitations of the status quo and present alternatives that could strengthen the field. In particular, we suggest that because human behavioral ecologists often consider multiple hypotheses, they should use statistical approaches that can quantify the evidence in empirical data for competing hypotheses. Although we focus on HBE, the principles of this paper apply broadly within biological anthropology.     

Joint inference for competing risks data using multiple endpoints

Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations and/or are of simultaneous interest. In clinical trials, often more than one competing event has meaningful clinical interpretations even though the trial effects of different events could be different or even opposite to each other. In this paper, we develop estimation procedures and inferential properties for the joint use of multiple cumulative incidence functions (CIFs). Additionally, by incorporating longitudinal marker information, we develop estimation and inference procedures for weighted CIFs and related metrics. The proposed methods are applied to a COVID-19 in-patient treatment clinical trial, where the outcomes of COVID-19 hospitalization are either death or discharge from the hospital, two competing events with completely different clinical implications.     

Shrunken p-values for assessing differential expression with applications to genomic data analysis

In many scientific problems involving high-throughput technology, inference must be made involving several hundreds or thousands of hypotheses. Recent attention has focused on how to address the multiple testing issue; much focus has been devoted toward the use of the false discovery rate. In this article, we consider an alternative estimation procedure titled shrunken p-values for assessing differential expression (SPADE). The estimators are motivated by risk considerations from decision theory and lead to a completely new method for adjustment in the multiple testing problem. In addition, the decision-theoretic framework can be used to derive a decision rule for controlling the number of false positive results. Some theoretical results are outlined. The proposed methodology is illustrated using simulation studies and with application to data from a prostate cancer gene expression profiling study.     

Rejecting "the given" in systematics

How morphology and systematics come together through morphological analysis, homology hypotheses and phylogenetic analysis is a topic of continuing debate. Some contemporary approaches reject biological evaluation of morphological characters and fall back on an atheoretical and putatively objective (but, in fact, phenetic) approach that defers to the test of congruence for homology assessment. We note persistent trends toward an uncritical empiricism (where evidence is believed to be immediately “given” in putatively theory‐free observation) and instrumentalism (where hypotheses of primary homology become mere instruments with little or no empirical foundation for choosing among competing phylogenetic hypotheses). We suggest that this situation is partly a consequence of the fact that the test of congruence and the related concept of total evidence have been inappropriately tied to a Popperian philosophy in modern systematics. Total evidence is a classical principle of inductive inference and does not imply a deductive test of homology. The test of congruence by itself is based philosophically on a coherence theory of truth (coherentism in epistemology), which is unconcerned with empirical foundation. We therefore argue that coherence of character statements (congruence of characters) is a necessary, but not a sufficient, condition to support or refute hypotheses of homology or phylogenetic relationship. There should be at least some causal grounding for homology hypotheses beyond mere congruence. Such causal grounding may be achieved, for example, through empirical investigations of comparative anatomy, developmental biology, functional morphology and secondary structure. © The Willi Hennig Society 2006.     

San Marco and evolutionary biology

Gould and Lewontin use San Marco, Venice, to criticise the adaptationist program in biology. Following their lead, the architectural term “spandrel” is now widely used in biology to denote a feature that is a necessary byproduct of other aspects of the organism. I review the debate over San Marco and argue that the spandrels are not necessary in the sense originally used by Gould and Lewontin. I conclude that almost all the claims that Gould makes about San Marco are wrong and that it is reasonable to view the architectural spandrel as an adaptation. The spandrels example has not provided a good illustration of why adaptive explanations should be avoided. In fact, it can be used as an example of how adaptive explanations can be dismissed even when there is evidence in their favour. I also discuss the use of the concept of a spandrel in biology.     

Adaptive neuro-fuzzy inference technique for estimation of light penetration in reservoirs

An adaptive neuro-fuzzy inference technique has been adopted to estimate light levels in a reservoir. The data were collected randomly from Doğanci Dam Reservoir over a number of years. The input data set is a matrix with vectors of time, depth, sampling location, and incident solar radiation. The output data set is a vector representing light measured at various depths. Randomization and logarithmic transformations have been applied as preprocessing. One-half of the data have been utilized for training; testing and validation steps utilized one-fourth each. An adaptive neuro-fuzzy inference system (ANFIS) has been built as a prediction model for light penetration. Very high correlation values between predictions and real values on light measurements with relatively low root mean square error values have been obtained for training, test, and validation data sets. Elimination of the overtraining problem was ensured by satisfying close root mean square error values for all sets.     

Probabilistic and multiple regression modeling of the biologist’s decision processes and its implications for medical diagnosis

Current psychological research into the inference (diagnostic) process is briefly reviewed, using as a vehicle an investigation of the prediction of the probability of success of hypothetical applicants to a graduate program in biology. Brunswik’s lens model and multiple regression analysis are used, as is a Bayesian approach. Four judges’ (biologists’) predictions are analyzed. Some general conclusions about inference, drawn from the current data in psychology, are presented. This investigation was supported by PHS Research Grant No. MH-17487-01 from the National Institute of Mental Health.     

Resource availability and plant diversity explain patterns of invasion of an exotic grass

Aims In this study, we examine two common invasion biology hypotheses—biotic resistance and fluctuating resource availability—to explain the patterns of invasion of an invasive grass, Microstegium vimineum.Methods We used 13-year-old deer exclosures in Great Smoky Mountains National Park, USA, to examine how chronic disturbance by deer browsing affects available resources, plant diversity, and invasion in an understory plant community. Using two replicate 1 m 2 plots in each deer browsed and unbrowsed area, we recorded each plant species present, the abundance per species, and the fractional percent cover of vegetation by the cover classes: herbaceous, woody, and graminoid. For each sample plot, we also estimated overstory canopy cover, soil moisture, total soil carbon and nitrogen, and soil pH as a measure of abiotic differences between plots.Important findings We found that plant community composition between chronically browsed and unbrowsed plots differed markedly. Plant diversity was 40% lower in browsed than in unbrowsed plots. At our sites, diversity explained 48% and woody plant cover 35% of the variation in M. vimineum abundance. In addition, we found 3.3 times less M. vimineum in the unbrowsed plots due to higher woody plant cover and plant diversity than in the browsed plots. A parsimonious explanation of these results indicate that disturbances such as herbivory may elicit multiple conditions, namely releasing available resources such as open space, light, and decreasing plant diversity, which may facilitate the proliferation of an invasive species. Finally, by testing two different hypotheses, this study addresses more recent calls to incorporate multiple hypotheses into research attempting to explain plant invasion.