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Many journals in ecology and evolutionary biology encourage or require authors to make their data and code available alongside articles. In this study we investigated how often this data and code could be used together, when both were available, to computationally reproduce results published in articles. We surveyed the data and code sharing practices of 177 meta-analyses published in ecology and evolutionary biology journals published between 2015-17: 60% of articles shared data only, 1% shared code only, and 15% shared both data and code. In each of the articles which had shared both (n = 26), we selected a target result and attempted to reproduce it. Using the shared data and code files, we successfully reproduced the targeted results in 27-73% of the 26 articles, depending on the stringency of the criteria applied for a successful reproduction. The results from this sample of meta-analyses in the 2015-17 literature can provide a benchmark for future meta-research studies gauging the computational reproducibility of published research in ecology and evolutionary biology.

Conservation science practitioners seek to preempt irreversible impacts on species, ecosystems, and social–ecological systems, requiring efficient and timely action even when data and understanding are unavailable, incomplete, dated, or biased. These challenges are exacerbated by the scientific community's capacity to consistently distinguish between reliable and unreliable evidence, including the recognition of questionable research practices (QRPs, or “questionable practices”), which may threaten the credibility of research, including harming trust in well‐designed and reliable scientific research. In this paper, we propose a “toolkit” for open and pluralistic conservation science, highlighting common questionable practices and sources of bias and indicating where remedies for these problems may be found. The toolkit provides an accessible resource for anyone conducting, reviewing, or using conservation research, to identify sources of false claims or misleading evidence that arise unintentionally, or through misunderstandings or carelessness in the application of scientific methods and analyses. We aim to influence editorial and review practices and hopefully to remedy problems before they are published or deployed in policy or conservation practice.

Journal peer review regulates the flow of ideas through an academic discipline and thus has the power to shape what a research community knows, actively investigates, and recommends to policymakers and the wider public. We might assume that editors can identify the 'best' experts and rely on them for peer review. But decades of research on both expert decision-making and peer review suggests they cannot. In the absence of a clear criterion for demarcating reliable, insightful, and accurate expert assessors of research quality, the best safeguard against unwanted biases and uneven power distributions is to introduce greater transparency and structure into the process. This paper argues that peer review would therefore benefit from applying a series of evidence-based recommendations from the empirical literature on structured expert elicitation. We highlight individual and group characteristics that contribute to higher quality judgements, and elements of elicitation protocols that reduce bias, promote constructive discussion, and enable opinions to be objectively and transparently aggregated.

Replication-an important, uncommon, and misunderstood practice-is gaining appreciation in psychology. Achieving replicability is important for making research progress. If findings are not replicable, then prediction and theory development are stifled. If findings are replicable, then interrogation of their meaning and validity can advance knowledge. Assessing replicability can be productive for generating and testing hypotheses by actively confronting current understandings to identify weaknesses and spur innovation. For psychology, the 2010s might be characterized as a decade of active confrontation. Systematic and multi-site replication projects assessed current understandings and observed surprising failures to replicate many published findings. Replication efforts highlighted sociocultural challenges such as disincentives to conduct replications and a tendency to frame replication as a personal attack rather than a healthy scientific practice, and they raised awareness that replication contributes to self-correction. Nevertheless, innovation in doing and understanding replication and its cousins, reproducibility and robustness, has positioned psychology to improve research practices and accelerate progress.

BACKGROUND: Various stakeholders are calling for increased availability of data and code from cancer research. However, it is unclear how commonly these products are shared, and what factors are associated with sharing. Our objective was to evaluate how frequently oncology researchers make data and code available and explore factors associated with sharing. METHODS: A cross-sectional analysis of a random sample of 306 cancer-related articles indexed in PubMed in 2019 which studied research subjects with a cancer diagnosis was performed. All articles were independently screened for eligibility by two authors. Outcomes of interest included the prevalence of affirmative sharing declarations and the rate with which declarations connected to data complying with key FAIR principles (e.g. posted to a recognised repository, assigned an identifier, data license outlined, non-proprietary formatting). We also investigated associations between sharing rates and several journal characteristics (e.g. sharing policies, publication models), study characteristics (e.g. cancer rarity, study design), open science practices (e.g. pre-registration, pre-printing) and subsequent citation rates between 2020 and 2021. RESULTS: One in five studies declared data were publicly available (59/306, 19%, 95% CI: 15-24%). However, when data availability was investigated this percentage dropped to 16% (49/306, 95% CI: 12-20%), and then to less than 1% (1/306, 95% CI: 0-2%) when data were checked for compliance with key FAIR principles. While only 4% of articles that used inferential statistics reported code to be available (10/274, 95% CI: 2-6%), the odds of reporting code to be available were 5.6 times higher for researchers who shared data. Compliance with mandatory data and code sharing policies was observed in 48% (14/29) and 0% (0/6) of articles, respectively. However, 88% of articles (45/51) included data availability statements when required. Policies that encouraged data sharing did not appe

Unreliable research programmes waste funds, time, and even the lives of the organisms we seek to help and understand. Reducing this waste and increasing the value of scientific evidence require changing the actions of both individual researchers and the institutions they depend on for employment and promotion. While ecologists and evolutionary biologists have somewhat improved research transparency over the past decade (e.g. more data sharing), major obstacles remain. In this commentary, we lift our gaze to the horizon to imagine how researchers and institutions can clear the path towards more credible and effective research programmes.

Numerous studies have demonstrated low but increasing rates of data and code sharing within medical and health research disciplines. However it remains unclear how commonly data and code are shared across all fields of medical and health research, as well as whether sharing rates are positively associated with implementation of progressive policies by publishers and funders, or growing expectations from the medical and health research community at large. Therefore this systematic review aims to synthesise the findings of medical and health science studies that have empirically investigated the prevalence of data or code sharing, or both. Objectives include the investigation of: (i) the prevalence of public sharing of research data and code alongside published articles (including preprints), (ii) the prevalence of private sharing of research data and code in response to reasonable requests, and (iii) factors associated with the sharing of either research output (e.g., the year published, the publisher’s policy on sharing, the presence of a data or code availability statement). It is hoped that the results will provide some insight into how often research data and code are shared publicly and privately, how this has changed over time, and how effective some measures such as the institution of data sharing policies and data availability statements have been in motivating researchers to share their underlying data and code.

Replication is a hallmark of scientific research. As replications of individual studies are resource intensive, techniques for predicting the replicability are required. We introduce a new technique to evaluating replicability, the repliCATS (Collaborative Assessments for Trustworthy Science) process, a structured expert elicitation approach based on the IDEA protocol. The repliCATS process is delivered through an underpinning online platform and applied to the evaluation of research claims in social and behavioural sciences. This process can be deployed for both rapid assessment of small numbers of claims, and assessment of high volumes of claims over an extended period. Pilot data suggests that the accuracy of the repliCATS process meets or exceeds that of other techniques used to predict replicability. An important advantage of the repliCATS process is that it collects qualitative data that has the potential to assist with problems like understanding the limits of generalizability of scientific claims. The repliCATS process has potential applications in alternative peer review and in the allocation of effort for replication studies.

Solutions to the crisis in confidence in the psychological literature have been proposed in many recent articles, including increased publication of replication studies, a solution that requires engagement by the psychology research community. We surveyed Australian and Italian academic research psychologists about the meaning and role of replication in psychology. When asked what they consider to be a replication study, nearly all participants (98% of Australians and 96% of Italians) selected options that correspond to a direct replication. Only 14% of Australians and 8% of Italians selected any options that included changing the experimental method. Majorities of psychologists from both countries agreed that replications are very important, that more replications should be done, that more resources should be allocated to them, and that they should be published more often. Majorities of psychologists from both countries reported that they or their students sometimes or often replicate studies, yet they also reported having no replication studies published in the prior 5 years. When asked to estimate the percentage of published studies in psychology that are replications, both Australians (with a median estimate of 13%) and Italians (with a median estimate of 20%) substantially overestimated the actual rate. When asked what constitute the main obstacles to replications, difficulty publishing replications was the most frequently cited obstacle, coupled with the high value given to innovative or novel research and the low value given to replication studies.

Unreliable research programmes waste funds, time, and even the lives of the organisms we seek to help and understand. Reducing this waste and increasing the value of scientific evidence require changing the actions of both individual researchers and the institutions they depend on for employment and promotion. While ecologists and evolutionary biologists have somewhat improved research transparency over the past decade (e.g. more data sharing), major obstacles remain. In this commentary, we lift our gaze to the horizon to imagine how researchers and institutions can clear the path towards more credible and effective research programmes.

Structured protocols offer a transparent and systematic way to elicit and combine/aggregate, probabilistic predictions from multiple experts. These judgements can be aggregated behaviourally or mathematically to derive a final group prediction. Mathematical rules (e.g., weighted linear combinations of judgments) provide an objective approach to aggregation. The quality of this aggregation can be defined in terms of accuracy, calibration and informativeness. These measures can be used to compare different aggregation approaches and help decide on which aggregation produces the "best" final prediction. When experts' performance can be scored on similar questions ahead of time, these scores can be translated into performance-based weights, and a performance-based weighted aggregation can then be used. When this is not possible though, several other aggregation methods, informed by measurable proxies for good performance, can be formulated and compared. Here, we develop a suite of aggregation methods, informed by previous experience and the available literature. We differentially weight our experts' estimates by measures of reasoning, engagement, openness to changing their mind, informativeness, prior knowledge, and extremity, asymmetry or granularity of estimates. Next, we investigate the relative performance of these aggregation methods using three datasets. The main goal of this research is to explore how measures of knowledge and behaviour of individuals can be leveraged to produce a better performing combined group judgment. Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.

In recent years there has been increased interest in replicating prior research. One of the biggest challenges to assessing replicability is the cost in resources and time that it takes to repeat studies. Thus there is an impetus to develop rapid elicitation protocols that can, in a practical manner, estimate the likelihood that research findings will successfully replicate. We employ a novel implementation of the IDEA ('Investigate', 'Discuss', 'Estimate' and 'Aggregate) protocol, realised through the repliCATS platform. The repliCATS platform is designed to scalably elicit expert opinion about replicability of social and behavioural science research. The IDEA protocol provides a structured methodology for eliciting judgements and reasoning from groups. This paper describes the repliCATS platform as a multi-user cloud-based software platform featuring (1) a technical implementation of the IDEA protocol for eliciting expert opinion on research replicability, (2) capture of consent and demographic data, (3) on-line training on replication concepts, and (4) exporting of completed judgements. The platform has, to date, evaluated 3432 social and behavioural science research claims from 637 participants.

Communication and advocacy approaches that influence attitudes and behaviors are key to addressing conservation problems, and the way an issue is framed can affect how people view, judge, and respond to an issue. Responses to conservation interventions can also be influenced by subtle wording changes in statements that may appeal to different values, activate social norms, influence a person's affect or mood, or trigger certain biases, each of which can differently influence the resulting engagement, attitudes, and behavior. We contend that by strategically considering how conservation communications are framed, they can be made more effective with little or no additional cost. Key framing considerations include, emphasizing things that matter to the audience, evoking helpful social norms, reducing psychological distance, leveraging useful biases, and, where practicable, testing messages. These lessons will help communicators think strategically about how to frame messages for greater effect.

Beef production is a major driver of biodiversity loss and greenhouse gas emissions globally, and multiple studies recommend reducing beef production and consumption. Although there have been significant efforts from the biodiversity conservation sector toward reducing beef‐production impacts, there has been comparatively much less engagement in reducing beef consumption. As a first step to address this gap and identify leverage points, we conducted a policy Delphi expert elicitation. We asked 16 multidisciplinary experts from research and practitioner backgrounds to propose interventions for reducing beef consumption in the United States. Experts generated and critiqued 20 interventions, creating a qualitative dataset that was thematically analyzed to allow the interventions to be prioritized. Effective, feasible interventions included changing perceived social norms, targeting food providers, and increasing the availability and quality of beef alternatives. This work introduces a conservation research agenda for reducing beef consumption and explores a structured process for prioritizing behavioral interventions.

Recent large-scale projects in other disciplines have shown that results often fail to replicate when studies are repeated. The conditions contributing to this problem are also present in ecology, but there have not been any equivalent replication projects. Here, we survey ecologists' understanding of and opinions about replication studies. The majority of ecologists in our sample considered replication studies to be important (97%), not prevalent enough (91%), worth funding even given limited resources (61%), and suitable for publication in all journals (62%). However, there is a disconnect between this enthusiasm and the prevalence of direct replication studies in the literature which is much lower (0.023%: Kelly 2019) than our participants' median estimate of 10%. This may be explained by the obstacles our participants identified including the difficulty of conducting replication studies and of funding and publishing them. We conclude by offering suggestions for how replications could be better integrated into ecological research.

Peer review practices differ substantially between journals and disciplines. This study presents the results of a survey of 322 editors of journals in ecology, economics, medicine, physics and psychology. We found that 49% of the journals surveyed checked all manuscripts for plagiarism, that 61% allowed authors to recommend both for and against specific reviewers, and that less than 6% used a form of open peer review. Most journals did not have an official policy on altering reports from reviewers, but 91% of editors identified at least one situation in which it was appropriate for an editor to alter a report. Editors were also asked for their views on five issues related to publication ethics. A majority expressed support for co-reviewing, reviewers requesting access to data, reviewers recommending citations to their work, editors publishing in their own journals, and replication studies. Our results provide a window into what is largely an opaque aspect of the scientific process. We hope the findings will inform the debate about the role and transparency of peer review in scholarly publishing.

Background: The events of 9/11 and the October 2002 National Intelligence Estimate on Iraq's Continuing Programs for Weapons of Mass Destruction precipitated fundamental changes within the United States Intelligence Community. As part of the reform, analytic tradecraft standards were revised and codified into a policy document - Intelligence Community Directive (ICD) 203 - and an analytic ombudsman was appointed in the newly created Office for the Director of National Intelligence to ensure compliance across the intelligence community. In this paper we investigate the untested assumption that the ICD203 criteria can facilitate reliable evaluations of analytic products. Methods: Fifteen independent raters used a rubric based on the ICD203 criteria to assess the quality of reasoning of 64 analytical reports generated in response to hypothetical intelligence problems. We calculated the intra-class correlation coefficients for single and group-aggregated assessments. Results: Despite general training and rater calibration, the reliability of individual assessments was poor. However, aggregate ratings showed good to excellent reliability. Conclusion: Given that real problems will be more difficult and complex than our hypothetical case studies, we advise that groups of at least three raters are required to obtain reliable quality control procedures for intelligence products. Our study sets limits on assessment reliability and provides a basis for further evaluation of the predictive validity of intelligence reports generated in compliance with the tradecraft standards.

Current global enthusiasm for urban greening and bringing nature back into cities is unprecedented. Evidence of the socioecological benefits of large, permanent greenspaces is mounting, but the collective potential for pop‐up parks (PUPs) – small, temporary greenspaces – to augment urban ecosystem services is unknown. To showcase the potential of PUPs, we first highlight a case study demonstrating how PUPs may enhance biodiversity in a densely urbanized area; we then review evidence linking the design of small greenspaces with positive social outcomes, including benefits to human well‐being. Finally, we emphasize how PUPs can function as socioecological laboratories to help inform urban design, and then propose a research agenda to better understand how PUPs may be optimally designed to provide benefits to humans and other species.

People interpret verbal expressions of probabilities (e.g. 'very likely') in different ways, yet words are commonly preferred to numbers when communicating uncertainty. Simply providing numerical translations alongside reports or text containing verbal probabilities should encourage consistency, but these guidelines are often ignored. In an online experiment with 924 participants, we compared four different formats for presenting verbal probabilities with the numerical guidelines used in the US Intelligence Community Directive (ICD) 203 to see whether any could improve the correspondence between the intended meaning and participants' interpretation ('in-context'). This extends previous work in the domain of climate science. The four experimental conditions we tested were: 1. numerical guidelines bracketed in text, e.g. X is very unlikely (05-20%), 2. click to see the full guidelines table in a new window, 3. numerical guidelines appear in a mouse over tool tip, and 4. no guidelines provided (control). Results indicate that correspondence with the ICD 203 standard is substantially improved only when numerical guidelines are bracketed in text. For this condition, average correspondence was 66%, compared with 32% in the control. We also elicited 'context-free' numerical judgements from participants for each of the seven verbal probability expressions contained in ICD 203 (i.e., we asked participants what range of numbers they, personally, would assign to those expressions), and constructed 'evidence-based lexicons' based on two methods from similar research, 'membership functions' and 'peak values', that reflect our large sample's intuitive translations of the terms. Better aligning the intended and assumed meaning of fuzzy words like 'unlikely' can reduce communication problems between the reporter and receiver of probabilistic information. In turn, this can improve decision making under uncertainty.

© 2019 Elsevier Ltd Changing human behavior and attitudes are key to conserving global biodiversity. Despite evidence from other disciplines that strategic messaging can influence behavior and attitudes, it remains unclear how to best design messages to benefit biodiversity. We conducted a systematic literature review to investigate the status of conservation messaging research, and to evaluate whether studies address essential elements of message design and theory from other disciplines. We show that academic interest in conservation messaging is growing rapidly. However, our results suggest that conservation scientists are not effectively drawing from the long-standing expertise of disciplines with well-established messaging techniques. Many studies do not draw on established behavior change theories or audience segmentation techniques. Given the urgent need to address the loss of biodiversity, we discuss how conservation messaging can draw on existing empirical and theoretical knowledge, with a focus on the application of established techniques used in messaging for pro-environmental behavior.

We surveyed 807 researchers (494 ecologists and 313 evolutionary biologists) about their use of Questionable Research Practices (QRPs), including cherry picking statistically significant results, p hacking, and hypothesising after the results are known (HARKing). We also asked them to estimate the proportion of their colleagues that use each of these QRPs. Several of the QRPs were prevalent within the ecology and evolution research community. Across the two groups, we found 64% of surveyed researchers reported they had at least once failed to report results because they were not statistically significant (cherry picking); 42% had collected more data after inspecting whether results were statistically significant (a form of p hacking) and 51% had reported an unexpected finding as though it had been hypothesised from the start (HARKing). Such practices have been directly implicated in the low rates of reproducible results uncovered by recent large scale replication studies in psychology and other disciplines. The rates of QRPs found in this study are comparable with the rates seen in psychology, indicating that the reproducibility problems discovered in psychology are also likely to be present in ecology and evolution.

INTRODUCTION: Natural resource management uses expert judgement to estimate facts that inform important decisions. Unfortunately, expert judgement is often derived by informal and largely untested protocols, despite evidence that the quality of judgements can be improved with structured approaches. We attribute the lack of uptake of structured protocols to the dearth of illustrative examples that demonstrate how they can be applied within pressing time and resource constraints, while also improving judgements. AIMS AND METHODS: In this paper, we demonstrate how the IDEA protocol for structured expert elicitation may be deployed to overcome operational challenges while improving the quality of judgements. The protocol was applied to the estimation of 14 future abiotic and biotic events on the Great Barrier Reef, Australia. Seventy-six participants with varying levels of expertise related to the Great Barrier Reef were recruited and allocated randomly to eight groups. Each participant provided their judgements using the four-step question format of the IDEA protocol ('Investigate', 'Discuss', 'Estimate', 'Aggregate') through remote elicitation. When the events were realised, the participant judgements were scored in terms of accuracy, calibration and informativeness. RESULTS AND CONCLUSIONS: The results demonstrate that the IDEA protocol provides a practical, cost-effective, and repeatable approach to the elicitation of quantitative estimates and uncertainty via remote elicitation. We emphasise that i) the aggregation of diverse individual judgements into pooled group judgments almost always outperformed individuals, and ii) use of a modified Delphi approach helped to remove linguistic ambiguity, and further improved individual and group judgements. Importantly, the protocol encourages review, critical appraisal and replication, each of which is required if judgements are to be used in place of data in a scientific context. The results add to the growing body of liter

Article impact statement: Continued development of conservation psychology is essential to addressing the challenges of biodiversity conservation.

Peer review is widely considered fundamental to maintaining the rigour of science, but it often fails to ensure transparency and reduce bias in published papers, and this systematically weakens the quality of published inferences. In part, this is because many reviewers are unaware of important questions to ask with respect to the soundness of the design and analyses, and the presentation of the methods and results; also some reviewers may expect others to be responsible for these tasks. We therefore present a reviewers’ checklist of ten questions that address these critical components. Checklists are commonly used by practitioners of other complex tasks, and we see great potential for the wider adoption of checklists for peer review, especially to reduce bias and facilitate transparency in published papers. We expect that such checklists will be well received by many reviewers.

Recent replication projects in other disciplines have uncovered disturbingly low levels of reproducibility, suggesting that those research literatures may contain unverifiable claims. The conditions contributing to irreproducibility in other disciplines are also present in ecology. These include a large discrepancy between the proportion of "positive" or "significant" results and the average statistical power of empirical research, incomplete reporting of sampling stopping rules and results, journal policies that discourage replication studies, and a prevailing publish-or-perish research culture that encourages questionable research practices. We argue that these conditions constitute sufficient reason to systematically evaluate the reproducibility of the evidence base in ecology and evolution. In some cases, the direct replication of ecological research is difficult because of strong temporal and spatial dependencies, so here, we propose metaresearch projects that will provide proxy measures of reproducibility.

Expert judgements are essential when time and resources are stretched or we face novel dilemmas requiring fast solutions. Good advice can save lives and large sums of money. Typically, experts are defined by their qualifications, track record and experience. The social expectation hypothesis argues that more highly regarded and more experienced experts will give better advice. We asked experts to predict how they will perform, and how their peers will perform, on sets of questions. The results indicate that the way experts regard each other is consistent, but unfortunately, ranks are a poor guide to actual performance. Expert advice will be more accurate if technical decisions routinely use broadly-defined expert groups, structured question protocols and feedback.

A statistically significant result, and a non-significant result may differ little, although significance status may tempt an interpretation of difference. Two studies are reported that compared interpretation of such results presented using null hypothesis significance testing (NHST), or confidence intervals (CIs). Authors of articles published in psychology, behavioral neuroscience, and medical journals were asked, via email, to interpret two fictitious studies that found similar results, one statistically significant, and the other non-significant. Responses from 330 authors varied greatly, but interpretation was generally poor, whether results were presented as CIs or using NHST. However, when interpreting CIs respondents who mentioned NHST were 60% likely to conclude, unjustifiably, the two results conflicted, whereas those who interpreted CIs without reference to NHST were 95% likely to conclude, justifiably, the two results were consistent. Findings were generally similar for all three disciplines. An email survey of academic psychologists confirmed that CIs elicit better interpretations if NHST is not invoked. Improved statistical inference can result from encouragement of meta-analytic thinking and use of CIs but, for full benefit, such highly desirable statistical reform requires also that researchers interpret CIs without recourse to NHST.

Error bars commonly appear in figures in publications, but experimental biologists are often unsure how they should be used and interpreted. In this article we illustrate some basic features of error bars and explain how they can help communicate data and assist correct interpretation. Error bars may show confidence intervals, standard errors, standard deviations, or other quantities. Different types of error bars give quite different information, and so figure legends must make clear what error bars represent. We suggest eight simple rules to assist with effective use and interpretation of error bars.

Since the mid-1980s, confidence intervals (CIs) have been standard in medical journals. We sought lessons for psychology from medicine's experience with statistical reform by investigating two attempts by Kenneth Rothman to change statistical practices. We examined 594 American Journal of Public Health (AJPH) articles published between 1982 and 2000 and 110 Epidemiology articles published in 1990 and 2000. Rothman's editorial instruction to report CIs and not p values was largely effective: In AJPH, sole reliance on p values dropped from 63% to 5%, and CI reporting rose from 10% to 54%; Epidemiology showed even stronger compliance. However, compliance was superficial: Very few authors referred to CIs when discussing results. The results of our survey support what other research has indicated: Editorial policy alone is not a sufficient mechanism for statistical reform. Achieving substantial, desirable change will require further guidance regarding use and interpretation of CIs and appropriate effect size measures. Necessary steps will include studying researchers' understanding of CIs, improving education, and developing empirically justified recommendations for improved statistical practice.