Archiving Primary Data: Solutions for Long-Term Studies

A collaborative and near-comprehensive North Pacific humpback whale photo-ID dataset

We present an ocean-basin-scale dataset that includes tail fluke photographic identification (photo-ID) and encounter data for most living individual humpback whales (Megaptera novaeangliae) in the North Pacific Ocean. The dataset was built through a broad collaboration combining 39 separate curated photo-ID catalogs, supplemented with community science data. Data from throughout the North Pacific were aggregated into 13 regions, including six breeding regions, six feeding regions, and one migratory corridor. All images were compared with minimal pre-processing using a recently developed image recognition algorithm based on machine learning through artificial intelligence; this system is capable of rapidly detecting matches between individuals with an estimated 97-99% accuracy. For the 2001-2021 study period, a total of 27,956 unique individuals were documented in 157,350 encounters. Each individual was encountered, on average, in 5.6 sampling periods (i.e., breeding and feeding seasons), with an annual average of 87% of whales encountered in more than one season. The combined dataset and image recognition tool represents a living and accessible resource for collaborative, basin-wide studies of a keystone marine mammal in a time of rapid ecological change.

Effect of storage temperature and duration on direct PCR amplification of various feather types and DBS matrices

The use of direct PCR has been pioneered over the last decade for DNA analysis of biological specimens of distinct origins. The information on how longer these specimens can be stored and amplified by direct PCR is however scanty. Such a piece of information could expedite research and diagnostic studies without compromising the reliability of results. The current study was therefore designed to analyze the effect of storage temperature and duration on direct PCR amplification of biological specimens having either low quantity or high quantity of DNA. Whole blood, dried blood spots (DBS), and feathers from chicken were stored for five years at three different temperatures, viz. room temperature (∼25 °C), 4 °C, and -20 °C. These samples were subjected to crude DNA extraction by diluting them in PBS buffer and heating at 98 °C after 1 day, 7 days, 15 days, 1 month, 3 months, 6 months, 1 year, 3 years and 5 years of storage. The crude DNA was PCR-amplified with the use of DNA sexing primers as well as DNA barcoding primers. Incubation at 98 °C for 10 min of any type of sample in PBS buffer was sufficient for crude DNA extraction. There was irrelevant impact of feather type, DBS matrix nature and storage temperature on amplification success over the period of analysis. It was possible to successfully accomplish the amplification of 96 samples with the use of routine PCR reagents within 3.5-6.0 hrs. In short, economical and fast genetic analysis of commonly used avian samples is feasible after their storage for longer time at room temperature.

Inferring the age of breeders from easily measurable variables

Age drives differences in fitness components typically due to lower performances of younger and senescent individuals, and changes in breeding age structure influence population dynamics and persistence. However, determining age and age structure is challenging in most species, where distinctive age features are lacking and available methods require substantial efforts or invasive procedures. Here we explore the potential to assess the age of breeders, or at least to identify young and senescent individuals, by measuring some breeding parameters partially driven by age (e.g. egg volume in birds). Taking advantage of a long-term population monitored seabird, we first assessed whether age influenced egg volume, and identified other factors driving this trait by using general linear models. Secondly, we developed and evaluated a machine learning algorithm to assess the age of breeders using measurable variables. We confirmed that both younger and older individuals performed worse (less and smaller eggs) than middle-aged individuals. Our ensemble training algorithm was only able to distinguish young individuals, but not senescent breeders. We propose to test the combined use of field monitoring, classic regression analysis and machine learning methods in other wild populations were measurable breeding parameters are partially driven by age, as a possible tool for assessing age structure in the wild.

Preserving avian blood and DNA sampled in the wild: A survey of personal experiences

Collecting and storing biological material from wild animals in a way that does not deteriorate DNA quality for subsequent analyses is instrumental for research in ecology and evolution. Our aims were to gather reports on the effectiveness of methods commonly used by researchers for the field collection and long‐term storage of blood samples and DNA extracts from wild birds. Personal experiences were collected with an online survey targeted specifically at researchers sampling wild birds. Many researchers experienced problems with blood sample storage but not with DNA extract storage. Storage issues generated problems with obtaining adequate DNA quality and sufficient DNA quantity for the targeted molecular analyses but were not related to season of blood sampling, access to equipment, transporting samples, temperature, and method of blood storage. Final DNA quality and quantity were also not affected by storage time before DNA extraction or the methods used to extract DNA. We discuss practical aspects of field collection and storage and provide some general recommendations, with a list of pros and cons of different preservation methods of avian blood samples and DNA extracts.

Slow improvement to the archiving quality of open datasets shared by researchers in ecology and evolution

Many leading journals in ecology and evolution now mandate open data upon publication. Yet, there is very little oversight to ensure the completeness and reusability of archived datasets, and we currently have a poor understanding of the factors associated with high-quality data sharing. We assessed 362 open datasets linked to first- or senior-authored papers published by 100 principal investigators (PIs) in the fields of ecology and evolution over a period of 7 years to identify predictors of data completeness and reusability (data archiving quality). Datasets scored low on these metrics: 56.4% were complete and 45.9% were reusable. Data reusability, but not completeness, was slightly higher for more recently archived datasets and PIs with less seniority. Journal open data policy, PI gender and PI corresponding author status were unrelated to data archiving quality. However, PI identity explained a large proportion of the variance in data completeness (27.8%) and reusability (22.0%), indicating consistent inter-individual differences in data sharing practices by PIs across time and contexts. Several PIs consistently shared data of either high or low archiving quality, but most PIs were inconsistent in how well they shared. One explanation for the high intra-individual variation we observed is that PIs often conduct research through students and postdoctoral researchers, who may be responsible for the data collection, curation and archiving. Levels of data literacy vary among trainees and PIs may not regularly perform quality control over archived files. Our findings suggest that research data management training and culture within a PI's group are likely to be more important determinants of data archiving quality than other factors such as a journal's open data policy. Greater incentives and training for individual researchers at all career stages could improve data sharing practices and enhance data transparency and reusability.

Unravelling the Stability of Nightingale Song Over Time and Space Using Open, Citizen Science and Shared Data

Open science approaches enable and facilitate the investigation of many scientific questions in bioacoustics, such as studies on the temporal and spatial evolution of song, as in vocal dialects. In contrast to previous dialect studies, which mostly focused on songbird species with a small repertoire, here we studied the common nightingale (Luscinia megarhynchos), a bird species with a complex and large repertoire. To study dialects on the population level in this species, we used recordings from four datasets: an open museum archive, a citizen science platform, a citizen science project, and shared recordings from academic researchers. We conducted to the date largest temporal and geographic dialect study of birdsong including recordings from 1930 to 2019 and from 13 European countries, with a geographical coverage of 2,652 km of linear distance. To examine temporal stability and spatial dialects, a catalog of 1,868 song types of common nightingales was created. Instead of dialects, we found a high degree of stability over time and space in both, the sub-categories of song and in the occurrence of song types. For example, the second most common song type in our datasets occurred over nine decades and across Europe. In our case study, open and citizen science data proved to be equivalent, and in some cases even better, than data shared by an academic research group. Based on our results, we conclude that the combination of diverse and open datasets was particularly useful to study the evolution of song in a bird species with a large repertoire.

Open Data Practices among Users of Primary Biodiversity Data

Presence-only biodiversity data are increasingly relied on in biodiversity, ecology, and conservation research, driven by growing digital infrastructures that support open data sharing and reuse. Recent reviews of open biodiversity data have clearly documented the value of data sharing, but the extent to which the biodiversity research community has adopted open data practices remains unclear. We address this question by reviewing applications of presence-only primary biodiversity data, drawn from a variety of sources beyond open databases, in the indexed literature. We characterize how frequently researchers access open data relative to data from other sources, how often they share newly generated or collated data, and trends in metadata documentation and data citation. Our results indicate that biodiversity research commonly relies on presence-only data that are not openly available and neglects to make such data available. Improved data sharing and documentation will increase the value, reusability, and reproducibility of biodiversity research.

The data-index: An author-level metric that values impactful data and incentivizes data sharing

Author-level metrics are a widely used measure of scientific success. The h-index and its variants measure publication output (number of publications) and research impact (number of citations). They are often used to influence decisions, such as allocating funding or jobs. Here, we argue that the emphasis on publication output and impact hinders scientific progress in the fields of ecology and evolution because it disincentivizes two fundamental practices: generating impactful (and therefore often long-term) datasets and sharing data. We describe a new author-level metric, the data-index, which values both dataset output (number of datasets) and impact (number of data-index citations), so promotes generating and sharing data as a result. We discuss how it could be implemented and provide user guidelines. The data-index is designed to complement other metrics of scientific success, as scientific contributions are diverse and our value system should reflect that both for the benefit of scientific progress and to create a value system that is more equitable, diverse, and inclusive. Future work should focus on promoting other scientific contributions, such as communicating science, informing policy, mentoring other scientists, and providing open-access code and tools.

Data Management and Sharing for Collaborative Science: Lessons Learnt From the Euromammals Initiative

The current and future consequences of anthropogenic impacts such as climate change and habitat loss on ecosystems will be better understood and therefore addressed if diverse ecological data from multiple environmental contexts are more effectively shared. Re-use requires that data are readily available to the scientific scrutiny of the research community. A number of repositories to store shared data have emerged in different ecological domains and developments are underway to define common data and metadata standards. Nevertheless, the goal is far from being achieved and many challenges still need to be addressed. The definition of best practices for data sharing and re-use can benefit from the experience accumulated by pilot collaborative projects. The Euromammals bottom-up initiative has pioneered collaborative science in spatial animal ecology since 2007. It involves more than 150 institutes to address scientific, management and conservation questions regarding terrestrial mammal species in Europe using data stored in a shared database. In this manuscript we present some key lessons that we have learnt from the process of making shared data and knowledge accessible to researchers and we stress the importance of data management for data quality assurance. We suggest putting in place a pro-active data review before data are made available in shared repositories via robust technical support and users’ training in data management and standards. We recommend pursuing the definition of common data collection protocols, data and metadata standards, and shared vocabularies with direct involvement of the community to boost their implementation. We stress the importance of knowledge sharing, in addition to data sharing. We show the crucial relevance of collaborative networking with pro-active involvement of data providers in all stages of the scientific process. Our main message is that for data-sharing collaborative efforts to obtain substantial and durable scientific returns, the goals should not only consist in the creation of e-infrastructures and software tools but primarily in the establishment of a network and community trust. This requires moderate investment, but over long-term horizons.

Four key challenges in the open-data revolution

In Focus: Culina, A., Adriaensen, F., Bailey, L. D., et al. (2021) Connecting the data landscape of long-term ecological studies: The SPI-Birds data hub. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.13388. Long-term, individual-based datasets have been at the core of many key discoveries in ecology, and calls for the collection, curation and release of these kinds of ecological data are contributing to a flourishing open-data revolution in ecology. Birds, in particular, have been the focus of international research for decades, resulting in a number of uniquely long-term studies, but accessing these datasets has been historically challenging. Culina et al. (2021) introduce an online repository of individual-level, long-term bird records with ancillary data (e.g. genetics), which will enable key ecological questions to be answered on a global scale. As well as these opportunities, however, we argue that the ongoing open-data revolution comes with four key challenges relating to the (1) harmonisation of, (2) biases in, (3) expertise in and (4) communication of, open ecological data. Here, we discuss these challenges and how key efforts such as those by Culina et al. are using FAIR (Findable, Accessible, Interoperable and Reproducible) principles to overcome them. The open-data revolution will undoubtedly reshape our understanding of ecology, but with it the ecological community has a responsibility to ensure this revolution is ethical and effective.

Estimating the reproducibility of social learning research published between 1955 and 2018

Reproducibility is integral to science, but difficult to achieve. Previous research has quantified low rates of data availability and results reproducibility across the biological and behavioural sciences. Here, we surveyed 560 empirical publications, published between 1955 and 2018 in the social learning literature, a research topic that spans animal behaviour, behavioural ecology, cultural evolution and evolutionary psychology. Data were recoverable online or through direct data requests for 30% of this sample. Data recovery declines exponentially with time since publication, halving every 6 years, and up to every 9 years for human experimental data. When data for a publication can be recovered, we estimate a high probability of subsequent data usability (87%), analytical clarity (97%) and agreement of published results with reproduced findings (96%). This corresponds to an overall rate of recovering data and reproducing results of 23%, largely driven by the unavailability or incompleteness of data. We thus outline clear measures to improve the reproducibility of research on the ecology and evolution of social behaviour.

Connecting the data landscape of long-term ecological studies: The SPI-Birds data hub

The integration and synthesis of the data in different areas of science is drastically slowed and hindered by a lack of standards and networking programmes. Long-term studies of individually marked animals are not an exception. These studies are especially important as instrumental for understanding evolutionary and ecological processes in the wild. Furthermore, their number and global distribution provides a unique opportunity to assess the generality of patterns and to address broad-scale global issues (e.g. climate change). To solve data integration issues and enable a new scale of ecological and evolutionary research based on long-term studies of birds, we have created the SPI-Birds Network and Database (www.spibirds.org)-a large-scale initiative that connects data from, and researchers working on, studies of wild populations of individually recognizable (usually ringed) birds. Within year and a half since the establishment, SPI-Birds has recruited over 120 members, and currently hosts data on almost 1.5 million individual birds collected in 80 populations over 2,000 cumulative years, and counting. SPI-Birds acts as a data hub and a catalogue of studied populations. It prevents data loss, secures easy data finding, use and integration and thus facilitates collaboration and synthesis. We provide community-derived data and meta-data standards and improve data integrity guided by the principles of Findable, Accessible, Interoperable and Reusable (FAIR), and aligned with the existing metadata languages (e.g. ecological meta-data language). The encouraging community involvement stems from SPI-Bird's decentralized approach: research groups retain full control over data use and their way of data management, while SPI-Birds creates tailored pipelines to convert each unique data format into a standard format. We outline the lessons learned, so that other communities (e.g. those working on other taxa) can adapt our successful model. Creating community-specific hubs (such as ours, COMADRE for animal demography, etc.) will aid much-needed large-scale ecological data integration.

Reported Individual Costs and Benefits of Sharing Open Data among Canadian Academic Faculty in Ecology and Evolution

Open data facilitate reproducibility and accelerate scientific discovery but are hindered by perceptions that researchers bear costs and gain few benefits from publicly sharing their data, with limited empirical evidence to the contrary. We surveyed 140 faculty members working in ecology and evolution across Canada's top 20 ranked universities and found that more researchers report benefits (47.9%) and neutral outcomes (43.6%) than costs (21.4%) from openly sharing data. The benefits were independent of career stage and gender, but men and early career researchers were more likely to report costs. We outline mechanisms proposed by the study participants to reduce the individual costs and increase the benefits of open data for faculty members.

Adaptive coloration in pied flycatchers (Ficedula hypoleuca)-The devil is in the detail

Understanding the origin and persistence of phenotypic variation within and among populations is a major goal in evolutionary biology. However, the eagerness to find unadulterated explanatory models in combination with difficulties in publishing replicated studies may lead to severe underestimations of the complexity of selection patterns acting in nature. One striking example is variation in plumage coloration in birds, where the default adaptive explanation often is that brightly colored individuals signal superior quality across environmental conditions and therefore always should be favored by directional mate choice. Here, we review studies on the proximate determination and adaptive function of coloration traits in male pied flycatchers (Ficedula hypoleuca). From numerous studies, we can conclude that the dark male color phenotype is adapted to a typical northern climate and functions as a dominance signal in male-male competition over nesting sites, and that the browner phenotypes are favored by relaxed intraspecific competition with more dominant male collared flycatchers (Ficedula albicollis) in areas where the two species co-occur. However, the role of avoidance of hybridization in driving character displacement in plumage between these two species may not be as important as initially thought. The direction of female choice on male coloration in pied flycatchers is not simply as opposite in direction in sympatry and allopatry as traditionally expected, but varies also in relation to additional contexts such as climate variation. While some of the heterogeneity in the observed relationships between coloration and fitness probably indicate type 1 errors, we strongly argue that environmental heterogeneity and context-dependent selection play important roles in explaining plumage color variation in this species, which probably also is the case in many other species studied in less detail.

Long-term mark-recapture and growth data for large-sized migratory brown trout (Salmo trutta) from Lake Mjøsa, Norway

Background

Long-term data from marked animals provide a wealth of opportunities for studies with high relevance to both basic ecological understanding and successful management in a changing world. The key strength of such data is that they allow us to quantify individual variation in vital rates (e.g. survival, growth, reproduction) and then link it mechanistically to dynamics at the population level. However, maintaining the collection of individual-based data over long time periods comes with large logistic efforts and costs and studies spanning over decades are therefore rare. This is the case particularly for migratory aquatic species, many of which are in decline despite their high ecological, cultural and economical value.

New information

This paper describes two unique publicly available time series of individual-based data originating from a 51-year mark-recapture study of a land-locked population of large-sized migratory brown trout (Salmotrutta) in Norway: the Hunder trout. In the period 1966-2015, nearly 14,000 adult Hunder trout have been captured and individually marked during their spawning migration from Lake Mjøsa to the river Gubrandsdalslågen. Almost a third of those individuals were later recaptured alive during a later spawning run and/or captured by fishermen and reported dead or alive. This has resulted in the first data series: a mark-recapture-recovery dataset spanning half a century and more than 18,000 capture records. The second data series consists of additional data on juvenile and adult growth and life-history schedules from half of the marked individuals, obtained by means of scale-sample analysis. The two datasets offer a rare long-term perspective on individuals and population dynamics and provide unique opportunities to gain insights into questions surrounding management, conservation and restoration of migratory salmonid populations and freshwater ecosystems.

CeutaOPEN, individual-based field observations of breeding snowy plovers Charadrius nivosus

Shorebirds (part of the order Charadriiformes) have a global distribution and exhibit remarkable variation in ecological and behavioural traits that are pertinent to many core questions in the fields of evolutionary ecology and conservation biology. Shorebirds are also relatively convenient to study in the wild as they are ground nesting and often occupy open habitats that are tractable to monitor. Here we present a database documenting the reproductive ecology of 1,647 individually marked snowy plovers (Charadrius nivosus) monitored between 2006 and 2016 at Bahía de Ceuta (23°54N, 106°57W) – an important breeding site in north-western Mexico. The database encompasses various morphological, behavioural, and fitness-related traits of males and females along with spatial and temporal population dynamics. This open resource will serve as an important data repository for addressing overarching questions in avian ecology and wetland conservation during an era of big data and global collaborative science.

Measurement(s)	morphometric parameter • movement behavioral quality • offspring survival measurement • parental behavior • sexual interaction trait • social behavior
Technology Type(s)	fieldwork
Factor Type(s)	year of data collection • date of data collection • time of data collection • location of data collection • individual identity • sex of individual • family identity • nest initiation date • nest hatch date
Sample Characteristic - Organism	Charadrius nivosus
Sample Characteristic - Environment	saline pan • salt contaminated soil • intermittent saline evaporation pond • saline evaporation pond • saline marsh
Sample Characteristic - Location	Bahía de Ceuta, Sinaloa, Mexico

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12173511

Is non-human species radiosensitivity in the lab a good indicator of that in the field? Making the comparison more robust

Ecological risk assessment has globally become the basis for environmental decision-making within government and industry for chemical substances. Regarding radioactive substances, recently revised International and European Basic Safety Standards are pushing the development of member state policy on environmental regulation in the field of radiological protection. Within this framework, existing derived effect benchmarks for ionising radiation and non-human species need to be more robust to reinforce their credibility when used as levels of exposure considered to be safe for the environment. Actually, the derivation of such benchmarks has mainly relied on laboratory studies from a limited number of species. Moreover lab species would be apparently less radiosensitive than for example terrestrial wildlife chronically exposed to ionising radiation in the Chernobyl Exclusion Zone. Additionally to the results of such comparison that still need to be confirmed, another way to challenge benchmarks is to improve the quality/quantity of radiotoxicity data constituting the basis for a statistically-based comparison. This is the major focus of this paper where we demonstrate through various examples how to make the comparison more robust (i) by analysing the discrepancy between lab and field at the taxonomic level rather than at the ecosystem level, (ii) by extending the knowledge base making use of acute radiotoxicity data, (iii) by identifying environmental factors modifying radiological dose-effect relationship in the field.

What Have Long-Term Field Studies Taught Us About Population Dynamics?

Long-term studies have been crucial to the advancement of population biology, especially our understanding of population dynamics. We argue that this progress arises from three key characteristics of long-term research. First, long-term data are necessary to observe the heterogeneity that drives most population processes. Second, long-term studies often inherently lead to novel insights. Finally, long-term field studies can serve as model systems for population biology, allowing for theory and methods to be tested under well-characterized conditions. We illustrate these ideas in three long-term field systems that have made outsized contributions to our understanding of population ecology, evolution, and conservation biology. We then highlight three emerging areas to which long-term field studies are well positioned to contribute in the future: ecological forecasting, genomics, and macrosystems ecology. Overcoming the obstacles associated with maintaining long-term studies requires continued emphasis on recognizing the benefits of such studies to ensure that long-term research continues to have a substantial impact on elucidating population biology.

On the ecological insights provided by a long-term study on an even longer-lived bird

In Focus: Weimerskirch, H. (2018). Linking demographic processes and foraging ecology in wandering albatross-Conservation implications. Journal of Animal Ecology, 87, 945-955. https://doi.org/10.1111/1365-2656.12817 Long-term individual-based studies are extremely valuable resources to study how life histories are shaped by selection on between-individual variation in the acquisition and allocation of resources. In this issue, Weimerskirch (2018) synthesises a 50-year study, uniquely including 20 years of individual-based movement tracking, of the majestic wandering albatross. The synthesis shows how variation in foraging distribution and efficiency in relation to sex and age is reflected in physiology, fitness and population dynamics, and how understanding of such patterns and processes can aid conservation efforts. It thereby exemplifies why long-term individual-based studies are especially productive and informative and require maintenance and safeguarding.

Quantifying the impact of public omics data

The amount of omics data in the public domain is increasing every year. Modern science has become a data-intensive discipline. Innovative solutions for data management, data sharing, and for discovering novel datasets are therefore increasingly required. In 2016, we released the first version of the Omics Discovery Index (OmicsDI) as a light-weight system to aggregate datasets across multiple public omics data resources. OmicsDI aggregates genomics, transcriptomics, proteomics, metabolomics and multiomics datasets, as well as computational models of biological processes. Here, we propose a set of novel metrics to quantify the attention and impact of biomedical datasets. A complete framework (now integrated into OmicsDI) has been implemented in order to provide and evaluate those metrics. Finally, we propose a set of recommendations for authors, journals and data resources to promote an optimal quantification of the impact of datasets.

Increasing amount of public omics data are important and valuable resources for the research community. Here, the authors develop a set of metrics to quantify the attention and impact of biomedical datasets and integrate them into the framework of Omics Discovery Index (OmicsDI).

Biodiversity data integration-the significance of data resolution and domain

Recent years have seen an explosion in the availability of biodiversity data describing the distribution, function, and evolutionary history of life on earth. Integrating these heterogeneous data remains a challenge due to large variations in observational scales, collection purposes, and terminologies. Here, we conceptualize widely used biodiversity data types according to their domain (what aspect of biodiversity is described?) and informational resolution (how specific is the description?). Applying this framework to major data providers in biodiversity research reveals a strong focus on the disaggregated end of the data spectrum, whereas aggregated data types remain largely underutilized. We discuss the implications of this imbalance for the scope and representativeness of current macroecological research and highlight the synergies arising from a tighter integration of biodiversity data across domains and resolutions. We lay out effective strategies for data collection, mobilization, imputation, and sharing and summarize existing frameworks for scalable and integrative biodiversity research. Finally, we use two case studies to demonstrate how the explicit consideration of data domain and resolution helps to identify biases and gaps in global data sets and achieve unprecedented taxonomic and geographical data coverage in macroecological analyses.

This Essay highlights data resolution as central property of biodiversity data that affects the precision and representativeness of macroecological inferences. It also discusses ways to maximize synergies among data types and showcases the potential of cross-resolution, cross-domain data integration.

A story of data won, data lost and data re-found: the realities of ecological data preservation

This paper discusses the process of retrieval and updating legacy data to allow on-line discovery and delivery. There are many pitfalls of institutional and non-institutional ecological data conservation over the long term. Interruptions to custodianship, old media, lost knowledge and the continuous evolution of species names makes resurrection of old data challenging. We caution against technological arrogance and emphasise the importance of international standards. We use a case study of a compiled set of continent-wide vegetation survey data for which, although the analyses had been published, the raw data had not. In the original study, publications containing plot data collected from the 1880s onwards had been collected, interpreted, digitised and integrated for the classification of vegetation and analysis of its conservation status across Australia. These compiled data are an extremely valuable national collection that demanded publishing in open, readily accessible online repositories, such as the Terrestrial Ecosystem Research Network (http://www.tern.org.au) and the Atlas of Living Australia (ALA: http://www.ala.org.au), the Australian node of the Global Biodiversity Information Facility (GBIF: http://www.gbif.org). It is hoped that the lessons learnt from this project may trigger a sober review of the value of endangered data, the cost of retrieval and the importance of suitable and timely archiving through the vicissitudes of technological change, so the initial unique collection investment enables multiple re-use in perpetuity.

Reply to the comment on "Synchronizing biological cycles as key to survival under a scenario of global change: The Common quail (Coturnix coturnix) strategy" by Rodriguez-Teijeiro et al

Two methodological criticisms of our paper "Synchronizing biological cycles as key to survival under a scenario of global change: The Common quail (Coturnix coturnix) strategy" (Nadal et al., 2018) were proposed in the comment by Rodriguez-Teijeiro et al. (2018) regarding: 1) our estimates of the mean date of arrival, duration of stay and departure stages in the different regions studied; and 2) the analyses carried out to correlate the phenology of the species with changes in the climate variables. The conceptual model that we presented relates the dynamics of this quail population, which moves between short periods of stays, and the spatio-temporal structure of their geographic distribution data, in order to understand the ecology of these birds and to link their movement and residency patterns with geographical area and climate conditions. The probability that quail are resident in a region on any particular date is a result of their overall ecological strategy. We believe that Rodríguez-Teijeiro et al. (2018) have misunderstood our model, leading to their criticism of the statistical tests that we applied.

Questionable research practices in ecology and evolution

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.

Environmental coupling of heritability and selection is rare and of minor evolutionary significance in wild populations

Predicting the rate of adaptation to environmental change in wild populations is important for understanding evolutionary change. However, predictions may be unreliable if the two key variables affecting the rate of evolutionary change-heritability and selection-are both affected by the same environmental variable. To determine how general such an environmentally induced coupling of heritability and selection is, and how this may influence the rate of adaptation, we made use of freely accessible, open data on pedigreed wild populations to answer this question at the broadest possible scale. Using 16 populations from 10 vertebrate species, which provided data on 50 traits (relating to body mass, morphology, physiology, behaviour and life history), we found evidence for an environmentally induced relationship between heritability and selection in only 6 cases, with weak evidence that this resulted in an increase or decrease in the expected selection response. We conclude that such a coupling of heritability and selection is unlikely to strongly affect evolutionary change, even though both heritability and selection are commonly postulated to be dependent on the environment.

The Missing Response to Selection in the Wild

Although there are many examples of contemporary directional selection, evidence for responses to selection that match predictions are often missing in quantitative genetic studies of wild populations. This is despite the presence of genetic variation and selection pressures – theoretical prerequisites for the response to selection. This conundrum can be explained by statistical issues with accurate parameter estimation, and by biological mechanisms that interfere with the response to selection. These biological mechanisms can accelerate or constrain this response. These mechanisms are generally studied independently but might act simultaneously. We therefore integrated these mechanisms to explore their potential combined effect. This has implications for explaining the apparent evolutionary stasis of wild populations and the conservation of wildlife.

Recent discoveries at the intersection of quantitative genetics and evolutionary ecology are challenging our views on the potential of wild populations to respond to selection.

Multiple biological mechanisms can disconnect genetic variation from the response to selection in the wild. We highlight areas for future research.

We provide an integrative framework that can be used to qualitatively assess the combined influence of these mechanisms on the response to selection.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Striving for transparent and credible research: practical guidelines for behavioral ecologists

Science is meant to be the systematic and objective study of the world but evidence suggests that scientific practices are sometimes falling short of this expectation. In this invited idea, we argue that any failure to conduct research according to a documented plan (lack of reliability) and/or any failure to ensure that reconducting the same project would provide the same finding (lack of reproducibility), will result in a low probability of independent studies reaching the same outcome (lack of replicability). After outlining the challenges facing behavioral ecology and science more broadly and incorporating advice from international organizations such as the Center for Open Science (COS), we present clear guidelines and tutorials on what we think open practices represent for behavioral ecologists. In addition, we indicate some of the currently most appropriate and freely available tools for adopting these practices. Finally, we suggest that all journals in our field, such as Behavioral Ecology, give additional weight to transparent studies and therefore provide greater incentives to align our scientific practices to our scientific values. Overall, we argue that producing demonstrably credible science is now fully achievable for the benefit of each researcher individually and for our community as a whole.

Combining community resurvey data to advance global change research

More and more ecologists have started to resurvey communities sampled in earlier decades to determine long-term shifts in community composition and infer the likely drivers of the ecological changes observed. However, to assess the relative importance of, and interactions among, multiple drivers joint analyses of resurvey data from many regions spanning large environmental gradients are needed. In this paper we illustrate how combining resurvey data from multiple regions can increase the likelihood of driver-orthogonality within the design and show that repeatedly surveying across multiple regions provides higher representativeness and comprehensiveness, allowing us to answer more completely a broader range of questions. We provide general guidelines to aid implementation of multi-region resurvey databases. In so doing, we aim to encourage resurvey database development across other community types and biomes to advance global environmental change research.