Implementing code review in the scientific workflow: Insights from ecology and evolutionary biology.

Code review increases reliability and improves reproducibility of research. As such, code review is an inevitable step in software development and is common in fields such as computer science. However, despite its importance, code review is noticeably lacking in ecology and evolutionary biology. This is problematic as it facilitates the propagation of coding errors and a reduction in reproducibility and reliability of published results. To address this, we provide a detailed commentary on how to effectively review code, how to set up your project to enable this form of review and detail its possible implementation at several stages throughout the research process. This guide serves as a primer for code review, and adoption of the principles and advice here will go a long way in promoting more open, reliable, and transparent ecology and evolutionary biology.

Environmental evidence in action: on the science and practice of evidence synthesis and evidence-based decision-making.

In civil society we expect that policy and management decisions will be made using the best available evidence. Yet, it is widely known that there are many barriers that limit the extent to which that occurs. One way to overcome these barriers is via robust, comprehensive, transparent and repeatable evidence syntheses (such as systematic reviews) that attempt to minimize various forms of bias to present a summary of existing knowledge for decision-making purposes. Relative to other disciplines (e.g., health care, education), such evidence-based decision-making remains relatively nascent for environment management despite major threats to humanity, such as the climate, pollution and biodiversity crises demonstrating that human well-being is inextricably linked to the biophysical environment. Fortunately, there are a growing number of environmental evidence syntheses being produced that can be used by decision makers. It is therefore an opportune time to reflect on the science and practice of evidence-based decision-making in environment management to understand the extent to which evidence syntheses are embraced and applied in practice. Here we outline a number of key questions related to the use of environmental evidence that need to be explored in an effort to enhance evidence-based decision-making. There is an urgent need for research involving methods from social science, behavioural sciences, and public policy to understand the basis for patterns and trends in environmental evidence use (or misuse or ignorance). There is also a need for those who commission and produce evidence syntheses, as well as the end users of these syntheses to reflect on their experiences and share them with the broader evidence-based practice community to identify needs and opportunities for advancing the entire process of evidence-based practice. It is our hope that the ideas shared here will serve as a roadmap for additional scholarship that will collectively enhance evidence-based decision-making and ultimately benefit the environment and humanity.

ROBITT: A tool for assessing the risk-of-bias in studies of temporal trends in ecology.

Aggregated species occurrence and abundance data from disparate sources are increasingly accessible to ecologists for the analysis of temporal trends in biodiversity. However, sampling biases relevant to any given research question are often poorly explored and infrequently reported; this can undermine statistical inference. In other disciplines, it is common for researchers to complete 'risk-of-bias' assessments to expose and document the potential for biases to undermine conclusions. The huge growth in available data, and recent controversies surrounding their use to infer temporal trends, indicate that similar assessments are urgently needed in ecology.We introduce ROBITT, a structured tool for assessing the 'Risk-Of-Bias In studies of Temporal Trends in ecology'. ROBITT has a similar format to its counterparts in other disciplines: it comprises signalling questions designed to elicit information on the potential for bias in key study domains. In answering these, users will define study inferential goal(s) and relevant statistical target populations. This information is used to assess potential sampling biases across domains relevant to the research question (e.g. geography, taxonomy, environment), and how these vary through time. If assessments indicate biases, then users must clearly describe them and/or explain what mitigating action will be taken.Everything that users need to complete a ROBITT assessment is provided: the tool, a guidance document and a worked example. Following other disciplines, the tool and guidance document were developed through a consensus-forming process across experts working in relevant areas of ecology and evidence synthesis.We propose that researchers should be strongly encouraged to include a ROBITT assessment when publishing studies of biodiversity trends, especially when using aggregated data. This will help researchers to structure their thinking, clearly acknowledge potential sampling issues, highlight where expert consultation is required and provide an opportunity to describe data checks that might go unreported. ROBITT will also enable reviewers, editors and readers to establish how well research conclusions are supported given a dataset combined with some analytical approach. In turn, it should strengthen evidence-based policy and practice, reduce differing interpretations of data and provide a clearer picture of the uncertainties associated with our understanding of reality.

Quantifying the checks and balances of collaborative governance systems for adaptive carnivore management.

Recovering or threatened carnivore populations are often harvested to minimise their impact on human activities, such as livestock farming or game hunting. Increasingly, harvest quota decisions involve a set of scientific, administrative and political institutions operating at national and sub-national levels whose interactions and collective decision-making aim to increase the legitimacy of management and ensure population targets are met. In practice, however, assessments of how quota decisions change between these different actors and what consequences these changes have on population trends are rare.We combine a state-space population modelling approach with an analysis of quota decisions taken at both regional and national levels between 2007 and 2018 to build a set of decision-making models that together predict annual harvest quota values for Eurasian lynx (Lynx lynx) in Norway.We reveal a tendency for administrative decision-makers to compensate for consistent quota increases by political actors, particularly when the lynx population size estimate is above the regional target. Using population forecasts based on the ensemble of decision-making models, we show that such buffering of political biases ensures lynx population size remains close to regional and national targets in the long term.Our results go beyond the usual qualitative assessment of collaborative governance systems for carnivore management, revealing a system of checks and balances that, in the case of lynx in Norway, ensures both multi-stakeholder participation and sustainable harvest quotas. Nevertheless, we highlight important inter-regional differences in decision-making and population forecasts, the socio-ecological drivers of which need to be better understood to prevent future population declines. Synthesis and applications. Our work analyses the sequence of decisions leading to yearly quotas for lynx harvest in Norway, highlighting the collaborative and structural processes that together shape harvest sustainability. In doing so, we provide a predictive framework to evaluate participatory decision-making processes in wildlife management, paving the way for scientists and decision-makers to collaborate more widely in identifying where decision biases might lie and how institutional arrangements can be optimised to minimise them. We emphasise, however, that this is only possible if wildlife management decisions are documented and transparent.

The evidence synthesis and meta-analysis in R conference (ESMARConf): levelling the playing field of conference accessibility and equitability.

Rigorous evidence is vital in all disciplines to ensure efficient, appropriate, and fit-for-purpose decision-making with minimised risk of unintended harm. To date, however, disciplines have been slow to share evidence synthesis frameworks, best practices, and tools amongst one another. Recent progress in collaborative digital and programmatic frameworks, such as the free and Open Source software R, have significantly expanded the opportunities for development of free-to-use, incrementally improvable, community driven tools to support evidence synthesis (e.g. EviAtlas, robvis, PRISMA2020 flow diagrams and metadat). Despite this, evidence synthesis (and meta-analysis) practitioners and methodologists who make use of R remain relatively disconnected from one another. Here, we report on a new virtual conference for evidence synthesis and meta-analysis in the R programming environment (ESMARConf) that aims to connect these communities. By designing an entirely free and online conference from scratch, we have been able to focus efforts on maximising accessibility and equity-making these core missions for our new community of practice. As a community of practice, ESMARConf builds on the success and groundwork of the broader R community and systematic review coordinating bodies (e.g. Cochrane), but fills an important niche. ESMARConf aims to maximise accessibility and equity of participants across regions, contexts, and social backgrounds, forging a level playing field in a digital, connected, and online future of evidence synthesis. We believe that everyone should have the same access to participation and involvement, and we believe ESMARConf provides a vital opportunity to push for equitability across disciplines, regions, and personal situations.

Citationchaser: A tool for transparent and efficient forward and backward citation chasing in systematic searching.

Systematic searching aims to find all possibly relevant research from multiple sources, the basis for an unbiased and comprehensive evidence base. Along with bibliographic databases, systematic reviewers use a variety of additional methods to minimise procedural bias. Citation chasing exploits connections between research articles to identify relevant records for a review by making use of explicit mentions of one article within another. Citation chasing is a popular supplementary search method because it helps to build on the work of primary research and review authors. It does so by identifying potentially relevant studies that might otherwise not be retrieved by other search methods; for example, because they did not use the review authors' search terms in the specified combinations in their titles, abstracts, or keywords. Here, we briefly provide an overview of citation chasing as a method for systematic reviews. Furthermore, given the challenges and high resource requirements associated with citation chasing, the limited application of citation chasing in otherwise rigorous systematic reviews, and the potential benefit of identifying terminologically disconnected but semantically linked research studies, we have developed and describe a free and open source tool that allows for rapid forward and backward citation chasing. We introduce citationchaser, an R package and Shiny app for conducting forward and backward citation chasing from a starting set of articles. We describe the sources of data, the backend code functionality, and the user interface provided in the Shiny app.

Using the Value of Information to improve conservation decision making.

Conservation decisions are challenging, not only because they often involve difficult conflicts among outcomes that people value, but because our understanding of the natural world and our effects on it is fraught with uncertainty. Value of Information (VoI) methods provide an approach for understanding and managing uncertainty from the standpoint of the decision maker. These methods are commonly used in other fields (e.g. economics, public health) and are increasingly used in biodiversity conservation. This decision-analytical approach can identify the best management alternative to select where the effectiveness of interventions is uncertain, and can help to decide when to act and when to delay action until after further research. We review the use of VoI in the environmental domain, reflect on the need for greater uptake of VoI, particularly for strategic conservation planning, and suggest promising areas for new research. We also suggest common reporting standards as a means of increasing the leverage of this powerful tool. The environmental science, ecology and biodiversity categories of the Web of Knowledge were searched using the terms 'Value of Information,' 'Expected Value of Perfect Information,' and the abbreviation 'EVPI.' Google Scholar was searched with the same terms, and additionally the terms decision and biology, biodiversity conservation, fish, or ecology. We identified 1225 papers from these searches. Included studies were limited to those that showed an application of VoI in biodiversity conservation rather than simply describing the method. All examples of use of VOI were summarised regarding the application of VoI, the management objectives, the uncertainties, the models used, how the objectives were measured, and the type of VoI. While the use of VoI appears to be on the increase in biodiversity conservation, the reporting of results is highly variable, which can make it difficult to understand the decision context and which uncertainties were considered. Moreover, it was unclear if, and how, the papers informed management and policy interventions, which is why we suggest a range of reporting standards that would aid the use of VoI. The use of VoI in conservation settings is at an early stage. There are opportunities for broader applications, not only for species-focussed management problems, but also for setting local or global research priorities for biodiversity conservation, making funding decisions, or designing or improving protected area networks and management. The long-term benefits of applying VoI methods to biodiversity conservation include a more structured and decision-focused allocation of resources to research.

Quantitative traceable temperature measurement using novel thermal imaging camera.

Conventional thermal imaging cameras, based on focal-plane array (FPA) sensors, exhibit inherent problems: such as stray radiation, cross-talk and the calibration uncertainty of ensuring each pixel behaves as if it were an identical temperature sensor. Radiation thermometers can largely overcome these issues, comprising of only a single detector element that can be optimised and calibrated. Although the latter approach can provide excellent accuracy for single-point temperature measurement, it does not provide a temperature image of the target object. In this work, we present a micromechanical systems (MEMS) mirror and silicon (Si) avalanche photodiode (APD) based single-pixel camera, capable of producing quantitative thermal images at an operating wavelength of 1 µm. This work utilises a custom designed f-theta wide-angle lens and MEMS mirror, to scan +/- 30° in both x- and y-dimensions, without signal loss due to vignetting at any point in the field of view (FOV). Our single-pixel camera is shown to perform well, with 3 °C size-of-source effect (SSE) related temperature error and can measure below 700 °C whilst achieving ± 0.5 °C noise related measurement uncertainty. Our measurements were calibrated and traceable to the International Temperature Scale of 1990 (ITS-90). The combination of low SSE and absence of vignetting enables quantitative temperature measurements over a spatial field with measurement uncertainty at levels lower than would be possible with FPA based thermal imaging cameras.

Quantitative thermal imaging using single-pixel Si APD and MEMS mirror.

Accurate quantitative temperature measurements are difficult to achieve using focal-plane array sensors. This is due to reflections inside the instrument and the difficulty of calibrating a matrix of pixels as identical radiation thermometers. Size-of-source effect (SSE), which is the dependence of an infrared temperature measurement on the area surrounding the target area, is a major contributor to this problem and cannot be reduced using glare stops. Measurements are affected by power received from outside the field-of-view (FOV), leading to increased measurement uncertainty. In this work, we present a micromechanical systems (MEMS) mirror based scanning thermal imaging camera with reduced measurement uncertainty compared to focal-plane array based systems. We demonstrate our flexible imaging approach using a Si avalanche photodiode (APD), which utilises high internal gain to enable the measurement of lower target temperatures with an effective wavelength of 1 µm and compare results with a Si photodiode. We compare measurements from our APD thermal imaging instrument against a commercial bolometer based focal-plane array camera. Our scanning approach results in a reduction in SSE related temperature error by 66 °C for the measurement of a spatially uniform 800 °C target when the target aperture diameter is increased from 10 to 20 mm. We also find that our APD instrument is capable of measuring target temperatures below 700 °C, over these near infrared wavelengths, with D* related measurement uncertainty of ± 0.5 °C.

Model selection and averaging in the assessment of the drivers of household food waste to reduce the probability of false positives.

Food waste from households contributes the greatest proportion to total food waste in developed countries. Therefore, food waste reduction requires an understanding of the socio-economic (contextual and behavioural) factors that lead to its generation within the household. Addressing such a complex subject calls for sound methodological approaches that until now have been conditioned by the large number of factors involved in waste generation, by the lack of a recognised definition, and by limited available data. This work contributes to food waste generation literature by using one of the largest available datasets that includes data on the objective amount of avoidable household food waste, along with information on a series of socio-economic factors. In order to address one aspect of the complexity of the problem, machine learning algorithms (random forests and boruta) for variable selection integrated with linear modelling, model selection and averaging are implemented. Model selection addresses model structural uncertainty, which is not routinely considered in assessments of food waste in literature. The main drivers of food waste in the home selected in the most parsimonious models include household size, the presence of fussy eaters, employment status, home ownership status, and the local authority. Results, regardless of which variable set the models are run on, point toward large households as being a key target element for food waste reduction interventions.

Design and realization of a wide field of view infrared scanning system with an integrated micro-electromechanical system mirror.

We present a wide field of view (FOV) infrared scanning system, designed for single-pixel near-infrared thermal imaging. The scanning system consisted of a two-axis micro-electromechanical system (MEMS) mirror that was incorporated within the lens. The optical system consisted of two groups of lenses and a silicon avalanche photodiode. The system was designed for both the production of thermal images and also to utilize the techniques of radiation thermometry to measure the absolute temperature of targets from 500°C to 1100°C. Our system has the potential for real-time image acquisition, with improved data acquisition electronics. The FOV of our scanning system was ±30° when fully utilizing the MEMS mirror's scanning angle of ±5°. The pixel FOV (calculated from the distance to target size ratio) was 100:1. The image quality was analyzed, including the modulation transfer function, spot diagrams, ray fan plots, lateral chromatic aberrations, distortion, relative illumination, and size-of-source effect. The instrument was fabricated in our laboratory, and one of the thermal images, which was taken with the new lens, is presented as an example of the instrument optical performance.