Matthew D. Moran

Bio: Matthew D. Moran is an academic researcher from Hendrix College. The author has contributed to research in topics: Ecosystem services & Population. The author has an hindex of 18, co-authored 67 publications receiving 2597 citations. Previous affiliations of Matthew D. Moran include University of Delaware.

Arguments for rejecting the sequential Bonferroni in ecological studies

Abstract: Interpretation of results that include multiple statistical tests has been an issue of great concern for some time in the ecological literature. The basic problem is that when multiple tests are undertaken, each at the same significance level ( ), the probability of achieving at least one significant result is greater than that significance level (Zaykin et al. 2002). Therefore, there is an increased probability of rejecting a null hypothesis when it would be inappropriate to do so. The typical solution to this problem has been lowering the values for the table (i.e. establishing a table-wide significance level) and therefore reducing the probability of a spurious result. Specifically, the most common procedure has been the application of the sequential Bonferroni adjustment (Holm 1979, Miller 1981, Rice 1989). Arguments in this essay address the problems of adjusting probability values for tables of multiple statistical tests, and more specifically argue for rejection of the sequential Bonferroni as a solution to this problem. Since the influential publication of Rice (1989), the sequential Bonferroni correction has become the primary method of addressing the problem of multiple statistical tests in ecological research. The sequential Bonferroni adjusts the table-wide p-value to keep it constant at 0.05, and subsequently reduces the probability of a spurious result. Although other methods exist for addressing tables of multiple statistical tests, the sequential Bonferroni has become the most commonly utilized process. However, this method has several flaws ranging from mathematical to logical to practical that argue for rejecting this method in ecological studies.

Predicting dissatisfaction after total hip arthroplasty: a study of 850 patients.

Abstract: We conducted a prospective cohort study investigating the rate and predictors of dissatisfaction among patients after primary total hip arthroplasty (THA). Eight hundred fifty patients were assessed preoperatively and 1 year postoperatively using Patient Reported Outcome Measures. There was a 7% rate of dissatisfaction after THA. After univariate analysis, depression, preoperative Short Form 12 mental component score, and symptomatic arthritis of another major joint predicted dissatisfaction at 1 year, but after multivariate analysis, only symptomatic arthritis in another major joint was significant. The development of a major complication did not predict dissatisfaction. Satisfaction also correlates strongly with postoperative functional scores, relief of pain, restoration of function, and success in meeting patient expectations. Pain relief and expectation management are critical in maximizing patient satisfaction after THA.

Top-down cascade from a bitrophic predator in an old-field community

Abstract: We tested the hypothesis that a bitrophic (third and fourth level) arthropod predator can exert a cascading, top—down influence on other arthropods and plants in an early successional old field. First—stadium mantids, Tenodera sinensis, were added to replicated open—field plots in numbers corresponding to naturally occurring egg hatch density and allowed to remain for $\approx 2$ mo. Sticky—trap dispersal barriers around both control and mantid—addition plots allowed us to monitor emigration of arthropods continuously during the experiment. Biomass of herbivores, carnivores, and plants, and abundances of arthropod taxa within plots were determined at the beginning, middle, and end of the experiment. The impact of mantids on the community was a top—down trophic cascade, beginning at the fourth trophic level and evident at each of the lower three levels. Mantids induced marked behavioral responses in other predators, but inteference among predators did not prevent the trophic cascade. The most common predators, cursorial spiders, emigrated from mantid addition plots in significantly greater numbers than from controls. This behavioral response may have resulted from avoidance of predation or competition. Mantids decreased biomass of herbivorous arthropods through predation, and this decrease in turn increased biomass of plants. Therefore, these generalist predators were able to decrease herbivory enough to affect plant growth. This and other recent studies indicate that top—down effects can be important in structuring terrestrial communities. Ours is the first example of a top—down cascade by a generalist arthropod predator in a nonagricultural ecosystem and illustrates the importance of detecting behavioral responses in studies of trophic interactions.

No net insect abundance and diversity declines across US Long Term Ecological Research sites

Abstract: Recent reports of dramatic declines in insect abundance suggest grave consequences for global ecosystems and human society. Most evidence comes from Europe, however, leaving uncertainty about insect population trends worldwide. We used >5,300 time series for insects and other arthropods, collected over 4–36 years at monitoring sites representing 68 different natural and managed areas, to search for evidence of declines across the United States. Some taxa and sites showed decreases in abundance and diversity while others increased or were unchanged, yielding net abundance and biodiversity trends generally indistinguishable from zero. This lack of overall increase or decline was consistent across arthropod feeding groups and was similar for heavily disturbed versus relatively natural sites. The apparent robustness of US arthropod populations is reassuring. Yet, this result does not diminish the need for continued monitoring and could mask subtler changes in species composition that nonetheless endanger insect-provided ecosystem services. Analysing >5,000 population abundance time series for insects and other arthropods from 68 sites within the US Long Term Ecological Research network, the authors find high variation but no overall trend in abundance and diversity among sites and taxa.

A trophic cascade in a diverse arthropod community caused by a generalist arthropod predator

Abstract: We tested the hypothesis that a generalist arthropod predator, Tenodera sinensis Saussure, could trigger a trophic cascade in an old-field ecosystem. These mantids had relatively weak effects on abundance and biomass of other carnivorous arthropods as a group. The effect of mantids on herbivores was stronger than on carnivores, mainly concentrated in Homoptera and Diptera. Herbivore load was reduced by mantids with the consequence that overall plant biomass (mainly grasses) was increased. Percapita interaction strengths between mantids and other arthropod taxa were, for the most part, weakly negative. Our study demonstrates that a significant trophic cascade can be triggered by a generalist predator even within the framework of a diverse community with relatively diffuse interactions.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

A farewell to Bonferroni: the problems of low statistical power and publication bias

Abstract: Recently, Jennions and Moller (2003) carried out a metaanalysis on statistical power in the field of behavioral ecology and animal behavior, reviewing 10 leading journals including Behavioral Ecology. Their results showed dismayingly low average statistical power (note that a meta-analytic review of statistical power is different from post hoc power analysis as criticized in Hoenig and Heisey, 2001). The statistical power of a null hypothesis (Ho) significance test is the probability that the test will reject Ho when a research hypothesis (Ha) is true. Knowledge of effect size is particularly important for statistical power analysis (for statistical power analysis, see Cohen, 1988; Nakagawa and Foster, in press). There are many kinds of effect size measures available (e.g., Pearson’s r, Cohen’s d, Hedges’s g), but most of these fall into one of two major types, namely the r family and the d family (Rosenthal, 1994). The r family shows the strength of relationship between two variables while the d family shows the size of difference between two variables. As a benchmark for research planning and evaluation, Cohen (1988) proposed ‘conventional’ values for small, medium, and large effects: r 1⁄4.10, .30, and .50 and d 1⁄4.20, .50, and .80, respectively (in the way that p values of .05, .01, and .001 are conventional points, although these conventional values of effect size have been criticized; e.g., Rosenthal et al., 2000). The meta-analysis on statistical power by Jennions and Moller (2003) revealed that, in the field of behavioral ecology and animal behavior, statistical power of less than 20% to detect a small effect and power of less than 50% to detect a medium effect existed. This means, for example, that the average behavioral scientist performing a statistical test has a greater probability of making a Type II error (or b) (i.e., not rejecting Ho when Ho is false; note that statistical power is equals to 1 2 b) than if they had flipped a coin, when an experiment effect is of medium size (i.e., r 1⁄4 .30, d 1⁄4 .50). Here, I highlight and discuss an implication of this low statistical power on one of the most widely used statistical procedures, Bonferroni correction (Cabin and Mitchell, 2000). Bonferroni corrections are employed to reduce Type I errors (i.e., rejecting Ho when Ho is true) when multiple tests or comparisons are conducted. Two kinds of Bonferroni procedures are commonly used. One is the standard Bonferroni procedure, where a modified significant criterion (a/k where k is the number of statistical tests conducted on given data) is used. The other is the sequential Bonferroni procedure, which was introduced by Holm (1979) and popularized in the field of ecology and evolution by Rice (1989) (see these papers for the procedure). For example, in a recent volume of Behavioral Ecology (vol. 13, 2002), nearly one-fifth of papers (23 out of 117) included Bonferroni corrections. Twelve articles employed the standard procedure while 11 articles employed the sequential procedure (10 citing Rice, 1989, and one citing Holm, 1979). A serious problem associated with the standard Bonferroni procedure is a substantial reduction in the statistical power of rejecting an incorrect Ho in each test (e.g., Holm, 1979; Perneger, 1998; Rice, 1989). The sequential Bonferroni procedure also incurs reduction in power, but to a lesser extent (which is the reason that the sequential procedure is used in preference by some researchers; Moran, 2003). Thus, both procedures exacerbate the existing problem of low power, identified by Jennions and Moller (2003). For example, suppose an experiment where both an experimental group and a control group consist of 30 subjects. After an experimental period, we measure five different variables and conduct a series of t tests on each variable. Even prior to applying Bonferroni corrections, the statistical power of each test to detect a medium effect is 61% (a 1⁄4 .05), which is less than a recommended acceptable 80% level (Cohen, 1988). In the field of behavioral ecology and animal behavior, it is usually difficult to use large sample sizes (in many cases, n , 30) because of practical and ethical reasons (see Still, 1992). When standard Bonferroni corrections are applied, the statistical power of each t test drops to as low as 33% (to detect a medium effect at a/5 1⁄4 .01). Although sequential Bonferroni corrections do not reduce the power of the tests to the same extent, on average (33–61% per t test), the probability of making a Type II error for some of the tests (b 1⁄4 1 2 power, so 39–66%) remains unacceptably high. Furthermore, statistical power would be even lower if we measured more than five variables or if we were interested in detecting a small effect. Bonferroni procedures appear to raise another set of problems. There is no formal consensus for when Bonferroni procedures should be used, even among statisticians (Perneger, 1998). It seems, in some cases, that Bonferroni corrections are applied only when their results remain significant. Some researchers may think that their results are ‘more significant’ if the results pass the rigor of Bonferroni corrections, although this is logically incorrect (Cohen, 1990, 1994; Yoccoz, 1991). Many researchers are already reluctant to report nonsignificant results ( Jennions and Moller, 2002a,b). The wide use of Bonferroni procedures may be aggravating the tendency of researchers not to present nonsignificant results, because presentation of more tests with nonsignificant results may make previously ‘significant’ results ‘nonsignificant’ under Bonferroni procedures. The more detailed research (i.e., research measuring more variables) researchers do, the less probability they have of finding significant results. Moran (2003) recently named this paradox as a hyper-Red Queen phenomenon (see the paper for more discussion on problems with the sequential method). Imagine that we conduct a study where we measure as many relevant variables as possible, 10 variables, for example. We find only two variables statistically significant. Then, what should we do? We could decide to write a paper highlighting these two variables (and not reporting the other eight at all) as if we had hypotheses about the two significant variables in the first place. Subsequently, our paper would be published. Alternatively, we could write a paper including all 10 variables. When the paper is reviewed, referees might tell us that there were no significant results if we had ‘appropriately’ employed Bonferroni corrections, so that our study would not be advisable for publication. However, the latter paper is Behavioral Ecology Vol. 15 No. 6: 1044–1045 doi:10.1093/beheco/arh107 Advance Access publication on June 30, 2004

Genetic Dissection of Complex Traits

Abstract: Medical genetics was revolutionized during the 1980s by the application of genetic mapping to locate the genes responsible for simple Mendelian diseases. Most diseases and traits, however, do not follow simple inheritance patterns. Geneticists have thus begun taking up the even greater challenge of the genetic dissection of complex traits. Four major approaches have been developed: linkage analysis, allele-sharing methods, association studies, and polygenic analysis of experimental crosses. This article synthesizes the current state of the genetic dissection of complex traits—describing the methods, limitations, and recent applications to biological problems.

A review of trait-mediated indirect interactions in ecological communities

Abstract: In this paper we review the empirical studies documenting trait-mediated indirect interactions (TMIIs) in food webs. Basic models and empirical approaches that form the foundation of our conceptualization of species interactions generally assume that interactions are an intrinsic property of the two interacting species and therefore are governed by their respective densities. However, if a species reacts to the presence of a second species by altering its phenotype, then the trait changes in the reacting species can alter the per capita effect of the reacting species on other species and, consequently, population density or fitness of the other species. Such trait-mediated indirect interactions can reinforce or oppose density-mediated effects and have been largely overlooked by community ecologists. We first briefly develop the case for the broad mechanistic basis for TMIIs and then review the direct evidence for TMIIs in various permutations of simple three- to four-species food webs. We find strong evid...

Trophic cascades revealed in diverse ecosystems.

Abstract: New studies are documenting trophic cascades in theoretically unlikely systems such as tropical forests and the open ocean. Together with increasing evidence of cascades, there is a deepening understanding of the conditions that promote and inhibit the transmission of predatory effects. These conditions include the relative productivity of ecosystems, presence of refuges and the potential for compensation. However, trophic cascades are also altered by humans. Analyses of the extirpation of large animals reveal loss of cascades, and the potential of conservation to restore not only predator populations but also the ecosystem-level effects that ramify from their presence.