Science PhD career preferences: levels, changes, and advisor encouragement

Even though academic research is often viewed as the preferred career path for PhD trained scientists, most U.S. graduates enter careers in industry, government, or "alternative careers." There has been a growing concern that these career patterns reflect fundamental imbalances between the supply of scientists seeking academic positions and the availability of such positions. However, while government statistics provide insights into realized career transitions, there is little systematic data on scientists' career preferences and thus on the degree to which there is a mismatch between observed career paths and scientists' preferences. Moreover, we lack systematic evidence whether career preferences adjust over the course of the PhD training and to what extent advisors exacerbate imbalances by encouraging their students to pursue academic positions. Based on a national survey of PhD students at tier-one U.S. institutions, we provide insights into the career preferences of junior scientists across the life sciences, physics, and chemistry. We also show that the attractiveness of academic careers decreases significantly over the course of the PhD program, despite the fact that advisors strongly encourage academic careers over non-academic careers. Our data provide an empirical basis for common concerns regarding labor market imbalances. Our results also suggest the need for mechanisms that provide PhD applicants with information that allows them to carefully weigh the costs and benefits of pursuing a PhD, as well as for mechanisms that complement the job market advice advisors give to their current students.     

Inference in ecology and evolution

Most ecologists and evolutionary biologists continue to rely heavily on null hypothesis significance testing, rather than on recently advocated alternatives, for inference. Here, we briefly review null hypothesis significance testing and its major alternatives. We identify major objectives of statistical analysis and suggest which analytical approaches are appropriate for each. Any well designed study can improve our understanding of biological systems, regardless of the inferential approach used. Nevertheless, an awareness of available techniques and their pitfalls could guide better approaches to data collection and broaden the range of questions that can be addressed. Although we should reduce our reliance on significance testing, it retains an important role in statistical education and is likely to remain fundamental to the falsification of scientific hypotheses.     

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.     

Effect size, confidence interval and statistical significance: a practical guide for biologists

Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.     

Advancing Ecosystem Science by Promoting Greater Use of Theory and Multiple Research Approaches in Graduate Education

Since the inaugural edition of Ecosystems was published in 1998, ecosystem science has undergone substantial changes including the development of new research methods and an increasing emphasis on collaborations across traditional academic boundaries. In response to this transformation, we reflect on the current state of theory in ecosystem science, and make recommendations for training the next generation of Ph.D.-level ecosystem scientists. Specifically, we call for increased integration of theory into ecosystem science and outline the utility of iterating between theory and data generated by observations, experiments, and quantitative models. We recommend exposing graduate students to these three major approaches for generating data and propose strategies that students, advisors, and departments can employ to ensure this exposure. Ultimately, a successful training program will provide students with an understanding of key theories related to ecosystem science and how they interact with data, an appreciation for the interconnectedness of approaches to scientific inference, and a well-developed skill set in at least one approach—thereby empowering them to confidently tackle our pressing environmental problems. Although this is a daunting list of goals, continuing to advance our understanding of how ecosystems function necessitates a rigorous and well-developed training program.     

Cultivating inclusive instructional and research environments in ecology and evolutionary science

As we strive to lift up a diversity of voices in science, it is important for ecologists, evolutionary scientists, and educators to foster inclusive environments in their research and teaching. Academics in science often lack exposure to research on best practices in diversity, equity, and inclusion and may not know where to start to make scientific environments more welcoming and inclusive. We propose that by approaching research and teaching with empathy, flexibility, and a growth mind‐set, scientists can be more supportive and inclusive of their colleagues and students. This paper provides guidance, explores strategies, and directs scientists to resources to better cultivate an inclusive environment in three common settings: the classroom, the research laboratory, and the field. As ecologists and evolutionary scientists, we have an opportunity to adapt our teaching and research practices in order to foster an inclusive educational ecosystem for students and colleagues alike.     

Conducting robust ecological analyses with climate data

Although the number of studies discerning the impact of climate change on ecological systems continues to increase, there has been relatively little sharing of the lessons learnt when accumulating this evidence. At a recent workshop entitled ‘Using climate data in ecological research’ held at the UK Met Office, ecologists and climate scientists came together to discuss the robust analysis of climate data in ecology. The discussions identified three common pitfalls encountered by ecologists: 1) selection of inappropriate spatial resolutions for analysis; 2) improper use of publically available data or code; and 3) insufficient representation of the uncertainties behind the adopted approach. Here, we discuss how these pitfalls can be avoided, before suggesting ways that both ecology and climate science can move forward. Our main recommendation is that ecologists and climate scientists collaborate more closely, on grant proposals and scientific publications, and informally through online media and workshops. More sharing of data and code (e.g. via online repositories), lessons and guidance would help to reconcile differing approaches to the robust handling of data. We call on ecologists to think critically about which aspects of the climate are relevant to their study system, and to acknowledge and actively explore uncertainty in all types of climate data. And we call on climate scientists to make simple estimates of uncertainty available to the wider research community. Through steps such as these, we will improve our ability to robustly attribute observed ecological changes to climate or other factors, while providing the sort of influential, comprehensive analyses that efforts to mitigate and adapt to climate change so urgently require.     

In support of null hypothesis significance testing

Many criticisms have been levelled at null hypothesis significance testing (NHST). It is argued here that although there is reason to doubt that data subjected only to NHST have been subjected to sufficient analysis, the search for clear answers to well-formulated questions derived from substantive hypotheses is well served by NHST. To reliably draw inferences from data, however, NHST may need to be complemented by additional methods of analysis, such as the use of confidence intervals and of estimates of the degree of association between independent and dependent variables. It is argued that these should be seen as complements of, rather than as substitutes for, NHST since they do not directly test the strength of evidence against a null hypothesis.     

The continuing misuse of null hypothesis significance testing in biological anthropology

There is over 60 years of discussion in the statistical literature concerning the misuse and limitations of null hypothesis significance tests (NHST). Based on the prevalence of NHST in biological anthropology research, it appears that the discipline generally is unaware of these concerns. The p values used in NHST usually are interpreted incorrectly. A p value indicates the probability of the data given the null hypothesis. It should not be interpreted as the probability that the null hypothesis is true or as evidence for or against any specific alternative to the null hypothesis. P values are a function of both the sample size and the effect size, and therefore do not indicate whether the effect observed in the study is important, large, or small. P values have poor replicability in repeated experiments. The distribution of p values is continuous and varies from 0 to 1.0. The use of a cut‐off, generally p ≤ 0.05, to separate significant from nonsignificant results, is an arbitrary dichotomization of continuous variation. In 2016, the American Statistical Association issued a statement of principles regarding the misinterpretation of NHST, the first time it has done so regarding a specific statistical procedure in its 180‐year history. Effect sizes and confidence intervals, which can be calculated for any data used to calculate p values, provide more and better information about tested hypotheses than p values and NHST.     

Ethical hurdles for translational research

The notion of translational research has gained considerable currency over the past few years. While such an approach promises great scientific and clinical advances, the penumbra of translational research tends to incorporate prioritizing scientific projects based upon their potential for translation; tight financial connections between sponsors, scientists and clinical investigators; and sometimes research involving biological approaches for which there is little experience determining safety. It is these aspects of translational research that raise some serious ethical challenges. In this report, we examine three specific areas that raise ethical questions: (1) the potential implications of prioritizing research objectives based on the potential for translation; (2) cautions related to moving from bench to bedside (and back again); and (3) unique questions for translational research initiatives in academic medical centers. Based on this examination, it is clear that the financial and ethical costs as well as benefits of taking a translational approach need to be considered. In the meantime, exquisite attention needs to be paid whenever translational research is likely to affect the traditional fiduciary responsibilities of scientists, clinicians and institutions to research subjects, patients and students. Successful mechanisms that might be developed to address any untoward effects should be shared and evaluated.     

How to work with children and animals: A guide for school‐based citizen science in wildlife research

Engaging school students in wildlife research through citizen science projects can be a win–win for scientists and educators. Not only does it provide a way for scientists to gather new data, but it can also contribute to science education and help younger generations become more environmentally aware. However, wildlife research can be challenging in the best of circumstances, and there are few guidelines available to help scientists create successful citizen science projects for school students. This paper explores the opportunities and challenges faced when developing school‐based citizen science projects in wildlife research by synthesising two sources of information. First, we conducted a small, school‐based citizen science project that investigated the effects of supplementary feeding on urban birds as a case study. Second, we reviewed the literature to develop a database of school‐based citizen science projects that address questions in wildlife ecology and conservation. Based on these activities, we present five lessons for scientists considering a school‐based citizen science project. Overall, we found that school‐based citizen science projects must be carefully designed to ensure reliable data are collected, students remain engaged, and the project is achievable under the logistical constraints presented by conducting wildlife research in a school environment. Ultimately, we conclude that school‐based citizen science projects can be a powerful way of collecting wildlife data while also contributing to the education and development of environmentally aware students.     

Good news for the people who love bad news: an analysis of the funding of the top 1% most highly cited ecologists

The most highly cited ecologists and environmental scientists provide both a benchmark and unique opportunity to consider the importance of research funding. Here, we use citation data and self‐reported funding levels to assess the relative importance of various factors in shaping productivity and potential impact. The elite were senior Americans, well funded, with large labs. In contrast to Canadian NSERC grant holders (not in the top 1%), citations per paper did not increase with higher levels of funding within the ecological elite. We propose that this is good news for several reasons. It suggests that the publications generated by the top ecologists and environmental scientists are subject to limitations, that higher volume of publications is always important, and that increased funding to ecologists in general can shift our discipline to wider research networks. As expected, collaboration was identified as an important factor for the elite, and hopefully, this serves as a positive incentive to funding agencies since it increases the visibility of their research.     

Does the Animal Welfare Act apply to free-ranging animals?

Despite the long-standing role that institutional animal care and use committees (IACUCs) have played in reviewing and approving studies at academic institutions, compliance with the Animal Welfare Act (AWA) is not always complete for government natural resource agencies that use free-ranging animals in research and management studies. Even at universities, IACUCs face uncertainties about what activities are covered and about how to judge proposed research on free-ranging animals. One reason for much of the confusion is the AWA vaguely worded exemption for "field studies." In particular, fish are problematic because of the AWA exclusion of poikilothermic animals. However, most university IACUCs review studies on all animals, and the Interagency Research Animal Committee (IRAC) has published the "IRAC Principles," which extend coverage to all vertebrates used by federal researchers. Despite this extended coverage, many scientists working on wild animals continue to view compliance with the AWA with little enthusiasm. IACUCs, IACUC veterinarians, wildlife veterinarians, and fish and wildlife biologists must learn to work together to comply with the law and to protect the privilege of using free-ranging animals in research.     

Hypothesis testing in animal social networks

Behavioural ecologists are increasingly using social network analysis to describe the social organisation of animal populations and to test hypotheses. However, the statistical analysis of network data presents a number of challenges. In particular the non-independent nature of the data violates the assumptions of many common statistical approaches. In our opinion there is currently confusion and uncertainty amongst behavioural ecologists concerning the potential pitfalls when hypotheses testing using social network data. Here we review what we consider to be key considerations associated with the analysis of animal social networks and provide a practical guide to the use of null models based on randomisation to control for structure and non-independence in the data.     

Graduate students in conservation biology: Bridging the research–implementation gap

There is a growing consensus that a gap exists between research conducted at academic institutions and information available to practitioners that implement research into conservation policy and practice. Here, I review common recommendations for bridging the Research–Implementation Gap in conservation biology, highlight the unique abilities of graduate students to contribute solutions to this problem, and propose ways research institutions and professionals can encourage graduate students to participate in this process. While some appropriately point out that the main purpose of graduate school is to focus on research, I argue that being exposed early to the broader issues of research and implementation enhances the graduate research experience, helps train students to become leaders in conservation science, and contributes both immediate and long-term solutions to the research–implementation problem.     

Euclidean distance matrix analysis: a coordinate-free approach for comparing biological shapes using landmark data.

For problems of classification and comparison in biological research, the primary focus is on the similarity of forms. A biological form can be conveniently defined as consisting of size and shape. Several approaches for comparing biological shapes using landmark data are available. Lele (1991a) critically discusses these approaches and proposes a new method based on the Euclidean distance matrix representation of the form of an object. The purpose of this paper is to extend this new methodology to the comparison of groups of objects. We develop the statistical versions of various concepts introduced by Lele (1991a) and use them for developing statistical procedures for testing the hypothesis of shape difference between biological forms. We illustrate the use of this method by studying morphological differences between normal children and those affected with Crouzon and Apert syndromes and craniofacial sexual dimorphism in Cebus apella.     

生态学假说试验验证的原假说困境

试验方法是生态学假说的主要验证方法之一,但也存在由原假说引发的质疑.Quinn和Dunham(1983)通过对Platt(1964)的假说-演绎模型进行分析,主张生态学不可能存在可以严格被试验验证的原假说.Fisher的证伪主义与Neyman-Pearson(N-P)的非判决性使得统计学原假说不能被严格验证;而生态过程中存在的不同于经典物理学的原假说H₀(α=1,β=0)与不同的备假说H₁′(α′=1,β′=0)的情况,使得生态学原假说也很难得到严格的实验验证.通过降低P值、谨慎选择原假说、对非原假说采取非中心化和双侧验证可分别缓解上述的原假说困境.但统计学的原假说显著性验证(NHST)不应等同于生态学假说中有关因果关系的逻辑证明方法.因此,现有大量基于NHST的生态学假说的方法研究和试验验证的结果与结论都不是绝对的逻辑可靠的.     

Citizen science and wildlife biology: Synergies and challenges

Citizen science (CS) has evolved over the past decades as a working method involving interested citizens in scientific research, for example by reporting observations, taking measurements or analysing data. In the past, research on animal behaviour has been benefitting from contributions of citizen scientists mainly in the field of ornithology but the full potential of CS in ecological and behavioural sciences is surely still untapped. Here, we present case studies that successfully applied CS to research projects in wildlife biology and discuss potentials and challenges experienced. Our case studies cover a broad range of opportunities: large‐scale CS projects with interactive online tools on bird song dialects, engagement of stakeholders as citizen scientists to reduce human–wildlife conflicts, involvement of students of primary and secondary schools in CS projects as well as collaboration with the media leading to successful recruitment of citizen scientists. Each case study provides a short overview of the scientific questions and how they were approached to showcase the potentials and challenges of CS in wildlife biology. Based on the experience of the case studies, we highlight how CS may support research in wildlife biology and emphasise the value of fostering communication in CS to improve recruitment of participants and to facilitate learning and mutual trust among different groups of interest (e.g., researchers, stakeholders, students). We further show how specific training for the participants may be needed to obtain reliable data. We consider CS as a suitable tool to enhance research in wildlife biology through the application of open science procedures (i.e., open access to articles and the data on publicly available repositories) to support transparency and sharing experiences.     

Programs for invasive research in North American zoos and aquariums

Zoo-based research in North America is an emerging field, which has progressed from an ad hoc approach in a small number of zoos to a coordinated, integrated network of scientists with recognized research programs in approximately one half of the accredited institutions in North America. The disciplines most active in these programs--veterinary medicine and pathology, nutrition, reproductive biology, contraception, and behavior--are now becoming coordinated in zoos through Scientific Advisory Groups. Zoos with research programs generally establish either an institutional animal care and use committee or another committee to evaluate research proposals. In addition to scientific merit and experimental design, zoos evaluate proposals based on factors such as priority by conservation program/identified need; direct effect on species conservation, species type, and appropriateness; availability and location of animals; operational requirements/logistics; communication between institutions; and available funding. Euthanasia is considered only in rare circumstances. Zoo-based research has evolved into an integral component in animal management and conservation programs by providing practical information that is used to improve animal care, well-being, health, and reproduction. However, the degree to which zoos participate in invasive research varies considerably among institutions, due not only to resource limitations but also to how the term "invasive" is defined and accepted at each institution. A more standardized approach among zoological institutions for examining and approving research projects that are supported by zoo-based conservation programs would greatly facilitate the wildlife research efforts of North American zoos.     

Statistical Ritual Versus Knowledge Accrual in Wildlife Science

Abstract: I hypothesized that statistical ritual has supplanted knowledge accrual as the sine qua non of wildlife science. Under the hypothesis, I deduced occurrence of 1) significance testing of the obvious and inconsequential, 2) quantitative debasement of research problems, and 3) publication of papers that largely lacked information but were methodologically impeccable. Articles in past and recent wildlife literature fit the deductions and supported the hypothesis. Thus, wildlife science is operating inefficiently because quantitative formalities are supplanting ecological information in technical articles. This problem can be corrected by a change of mindset in authors, referees, and editors. The change entails less emphasis on quantitative ritual and more emphasis on information that aids in understanding and explaining nature and managing wildlife.