Citizen science and wildlife biology: Synergies and challenges

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

A survey of long‐term terrestrial ecology studies in Australia

Long‐term ecological studies (LTES) are critical for understanding and managing landscapes. To identify important research gaps, facilitate collaborations and communicate results, several countries have established long‐term ecological research networks. A few initiatives to create such a network in Australia have been undertaken, but relatively few published data exist on the current state of LTES in Australia. In this paper, we present the results of an online survey of terrestrial LTES projects sent to academic, government and non‐governmental organization‐based researchers across Australia. We asked questions pertaining to the focus, scope, support and outcomes of LTES spanning 7 years or longer. Based on the information reported from 85 Australian LTES, we: (i) identify the biomes, processes and species that are under‐represented in the current body of research; (ii) discuss important contributing factors to the successful development and survival of these projects; and (iii) make recommendations to help increase the productivity and influence of LTES across research, management and policy sectors.     

Data integration in the era of omics: current and future challenges

To integrate heterogeneous and large omics data constitutes not only a conceptual challenge but a practical hurdle in the daily analysis of omics data. With the rise of novel omics technologies and through large-scale consortia projects, biological systems are being further investigated at an unprecedented scale generating heterogeneous and often large data sets. These data-sets encourage researchers to develop novel data integration methodologies. In this introduction we review the definition and characterize current efforts on data integration in the life sciences. We have used a web-survey to assess current research projects on data-integration to tap into the views, needs and challenges as currently perceived by parts of the research community.     

Different facets of the same niche: Integrating citizen science and scientific survey data to predict biological invasion risk under multiple global change drivers

Citizen science initiatives have been increasingly used by researchers as a source of occurrence data to model the distribution of alien species. Since citizen science presence-only data suffer from some fundamental issues, efforts have been made to combine these data with those provided by scientifically structured surveys. Surprisingly, only a few studies proposing data integration evaluated the contribution of this process to the effective sampling of species' environmental niches and, consequently, its effect on model predictions on new time intervals. We relied on niche overlap analyses, machine learning classification algorithms and ecological niche models to compare the ability of data from citizen science and scientific surveys, along with their integration, in capturing the realized niche of 13 invasive alien species in Italy. Moreover, we assessed differences in current and future invasion risk predicted by each data set under multiple global change scenarios. We showed that data from citizen science and scientific surveys captured similar species niches though highlighting exclusive portions associated with clearly identifiable environmental conditions. In terrestrial species, citizen science data granted the highest gain in environmental space to the pooled niches, determining an increased future biological invasion risk. A few aquatic species modelled at the regional scale reported a net loss in the pooled niches compared to their scientific survey niches, suggesting that citizen science data may also lead to contraction in pooled niches. For these species, models predicted a lower future biological invasion risk. These findings indicate that citizen science data may represent a valuable contribution to predicting future spread of invasive alien species, especially within national-scale programmes. At the same time, citizen science data collected on species poorly known to citizen scientists, or in strictly local contexts, may strongly affect the niche quantification of these taxa and the prediction of their future biological invasion risk.     

Botanical gardens and citizen science: An (as yet) under-exploited potential

Participation of citizens to research activities probably began with a “Christmas bird count” in 1900. Citizen science (CS) activities can aim at several purposes: long-term monitoring, environmental education, preservation of traditional ecological knowledge. Citizen scientists can collect data, support scientists in the field, involve decision-makers, plan new research activities, etc. While CS may have critical issues, especially as far as data quality is concerned, it has several relevant advantages as well (reduced costs, production of “big data”, awareness raising, etc.). However, especially in Europe, there is still an under-exploited potential for botanical gardens to act as drivers for CS initiatives.     

Extending Standards for Genomics and Metagenomics Data: A Research Coordination Network for the Genomic Standards Consortium (RCN4GSC)

Through a newly established Research Coordination Network for the Genomic Standards Consortium (RCN4GSC), the GSC will continue its leadership in establishing and integrating genomic standards through community-based efforts. These efforts, undertaken in the context of genomic and metagenomic research aim to ensure the electronic capture of all genomic data and to facilitate the achievement of a community consensus around collecting and managing relevant contextual information connected to the sequence data. The GSC operates as an open, inclusive organization, welcoming inspired biologists with a commitment to community service. Within the collaborative framework of the ongoing, international activities of the GSC, the RCN will expand the range of research domains engaged in these standardization efforts and sustain scientific networking to encourage active participation by the broader community. The RCN4GSC, funded for five years by the US National Science Foundation, will primarily support outcome-focused working meetings and the exchange of early-career scientists between GSC research groups in order to advance key standards contributions such as GCDML. Focusing on the timely delivery of the extant GSC core projects, the RCN will also extend the pioneering efforts of the GSC to engage researchers active in developing ecological, environmental and biodiversity data standards. As the initial goals of the GSC are increasingly achieved, promoting the comprehensive use of effective standards will be essential to ensure the effective use of sequence and associated data, to provide access for all biologists to all of the information, and to create interdisciplinary opportunities for discovery. The RCN will facilitate these implementation activities through participation in major scientific conferences and presentations on scientific advances enabled by community usage of genomic standards.     

The EcoData Retriever: Improving Access to Existing Ecological Data

Ecological research relies increasingly on the use of previously collected data. Use of existing datasets allows questions to be addressed more quickly, more generally, and at larger scales than would otherwise be possible. As a result of large-scale data collection efforts, and an increasing emphasis on data publication by journals and funding agencies, a large and ever-increasing amount of ecological data is now publicly available via the internet. Most ecological datasets do not adhere to any agreed-upon standards in format, data structure or method of access. Some may be broken up across multiple files, stored in compressed archives, and violate basic principles of data structure. As a result acquiring and utilizing available datasets can be a time consuming and error prone process. The EcoData Retriever is an extensible software framework which automates the tasks of discovering, downloading, and reformatting ecological data files for storage in a local data file or relational database. The automation of these tasks saves significant time for researchers and substantially reduces the likelihood of errors resulting from manual data manipulation and unfamiliarity with the complexities of individual datasets.     

Long-term studies of freshwater macroinvertebrates: a review of the frequency, duration and ecological significance

1. The importance of a long‐term ecological perspective is well documented, yet the availability of long‐term data remains limited. This paper highlights the value of long‐term ecological studies of freshwater macroinvertebrates by reviewing both the availability of long‐term data and recent ecological contributions based on them. 2. A survey of recent literature on stream macroinvertebrates identified 46 papers published between 1987 and 2004 that included long‐term (i.e. ≥5 years) data. Most recently published long‐term studies of stream macroinvertebrates began collecting data in the 1970s and 1980s and their duration (time between first and last year sampled) was relatively brief (median = 9 years, maximum = 96 years). Most studies did not expand their temporal perspective by incorporating older data collected by other researchers. 3. Recent long‐term studies of macroinvertebrates have made major contributions to our understanding of interannual variation and cycles, complex abiotic and biotic interactions, and natural and anthropogenic disturbance and recovery. Without these studies, we would know much less about the magnitude of natural temporal variation, the importance of physical and biological disturbance and interactions, the role of pathogens and introduced species, the overall impact of pollution and the effectiveness of protection and remediation efforts. 4. If we are to encourage long‐term perspectives in our science, we need to facilitate the transfer of individual studies, as well as knowledge and data, among scientists. This includes efforts to archive and annotate data more effectively, so that they can be more easily incorporated into future research.     

A framework for the practical science necessary to restore sustainable,resilient, and biodiverse ecosystems

Demand for restoration of resilient, self‐sustaining, and biodiverse natural ecosystems as a conservation measure is increasing globally; however, restoration efforts frequently fail to meet standards appropriate for this objective. Achieving these standards requires management underpinned by input from diverse scientific disciplines including ecology, biotechnology, engineering, soil science, ecophysiology, and genetics. Despite increasing restoration research activity, a gap between the immediate needs of restoration practitioners and the outputs of restoration science often limits the effectiveness of restoration programs. Regrettably, studies often fail to identify the practical issues most critical for restoration success. We propose that part of this oversight may result from the absence of a considered statement of the necessary practical restoration science questions. Here we develop a comprehensive framework of the research required to bridge this gap and guide effective restoration. We structure questions in five themes: (1) setting targets and planning for success, (2) sourcing biological material, (3) optimizing establishment, (4) facilitating growth and survival, and (5) restoring resilience, sustainability, and landscape integration. This framework will assist restoration practitioners and scientists to identify knowledge gaps and develop strategic research focused on applied outcomes. The breadth of questions highlights the importance of cross‐discipline collaboration among restoration scientists, and while the program is broad, successful restoration projects have typically invested in many or most of these themes. Achieving restoration ecology's goal of averting biodiversity losses is a vast challenge: investment in appropriate science is urgently needed for ecological restoration to fulfill its potential and meet demand as a conservation tool.     

GO‐FAANG meeting: a Gathering On Functional Annotation of Animal Genomes

The Functional Annotation of Animal Genomes (FAANG) Consortium recently held a Gathering On FAANG (GO‐FAANG) Workshop in Washington, DC on October 7–8, 2015. This consortium is a grass‐roots organization formed to advance the annotation of newly assembled genomes of domesticated and non‐model organisms ( www.faang.org ). The workshop gathered together from around the world a group of 100+ genome scientists, administrators, representatives of funding agencies and commodity groups to discuss the latest advancements of the consortium, new perspectives, next steps and implementation plans. The workshop was streamed live and recorded, and all talks, along with speaker slide presentations, are available at www.faang.org . In this report, we describe the major activities and outcomes of this meeting. We also provide updates on ongoing efforts to implement discussions and decisions taken at GO‐FAANG to guide future FAANG activities. In summary, reference datasets are being established under pilot projects; plans for tissue sets, morphological classification and methods of sample collection for different tissues were organized; and core assays and data and meta‐data analysis standards were established.     

Wetland Restoration in the New Millennium: Do Research Efforts Match Opportunities?

Of 311 papers on wetland restoration, only 15 concerned large‐scale experimentation in restoration sites. Most papers described what happened, reported on small field experiments, or discussed restoration targets. While these are important topics, our opinion is that we lose significant opportunities to learn how to recover populations, community structure, and ecosystem processes, and we limit our ability to document variability and whole‐system responses, when we do not experiment at large scales. We suggest that, wherever possible, large projects facilitate field tests of alternative restoration approaches. Furthermore, we encourage researchers to take advantage of major restoration efforts by conducting large field experiments, assessing multiple responses, and offering restoration guidance in an adaptive framework.     

Parasite genome initiatives

During 1993-1994, scientists from developing and developed countries planned and initiated a number of parasite genome projects and several consortiums for the mapping and sequencing of these medium-sized genomes were established, often based on already ongoing scientific collaborations. Financial and other support came from WHO/TDR, Wellcome Trust and other funding agencies. Thus, the genomes of Plasmodium falciparum, Schistosoma mansoni, Trypanosoma cruzi, Leishmania major, Trypanosoma brucei, Brugia malayi and other pathogenic nematodes are now under study. From an initial phase of network formation, mapping efforts and resource building (EST, GSS, phage, cosmid, BAC and YAC library constructions), sequencing was initiated in gene discovery projects but soon also on a small chromosome, and now on a fully fledged genome scale. Proteomics, functional analysis, genetic manipulation and microarray analysis are ongoing to different degrees in the respective genome initiatives, and as the funding for the whole genome sequencing becomes secured, most of the participating laboratories, apart from larger sequencing centres, become oriented to post-genomics. Bioinformatics networks are being expanded, including in developing countries, for data mining, annotation and in-depth analysis.     

From forest fires to fisheries management: Anthropology,conservation biology,and historical ecology

Human‐environmental relationships have long been of interest to a variety of scientists, including ecologists, biologists, anthropologists, and many others. 1 , 2 In anthropology, this interest was especially prevalent among cultural ecologists of the 1970s and earlier, who tended to explain culture as the result of techno‐environmental constraints. 3 More recently researchers have used historical ecology, an approach that focuses on the long‐term dialectical relationship between humans and their environments, as well as long‐term prehuman ecological datasets. 4 - 7 An important contribution of anthropology to historical ecology is that anthropological datasets dealing with ethnohistory, traditional ecological knowledge, and human skeletal analysis, as well as archeological datasets on faunal and floral remains, artifacts, geochemistry, and stratigraphic analysis, provide a deep time perspective (across decades, centuries, and millennia) on the evolution of ecosystems and the place of people in those larger systems. Historical ecological data also have an applied component that can provide important information on the relative abundances of flora and fauna, changes in biogeography, alternations in food webs, landscape evolution, and much more.     

A workflow to integrate ecological monitoring data from different sources

Programs and initiatives aiming to protect biodiversity and ecosystems have increased over the last decades in response to their decline. Most of these are based on monitoring data to quantitatively describe trends in biodiversity and ecosystems. The estimation of such trends, at large scales, requires the integration of numerous data from multiple monitoring sites. However, due to the high heterogeneity of data formats and the resulting lack of interoperability, the data integration remains sparsely used and synthetic analyses are often limited to a restricted part of the data available.Here we propose a workflow, comprising four main steps, from data gathering to quality control, to better integrate ecological monitoring data and to create a synthetic dataset that will make it possible to analyse larger sets of monitoring data, including unpublished data.The workflow was designed and applied in the production of the Status of Coral Reefs of the World: 2020 report, where more than two hundred individual datasets were integrated to assess the status and trends of hard coral cover at the global scale. The workflow was applied to two case studies and associated R codes, based on the experience acquired during the production of this report.The proposed workflow allows for the integration of datasets with different levels of taxonomic and spatial precision, with a high degree of reproducibility. It provides a conceptual and technical framework for the integration of ecological monitoring data, allowing for the estimation of temporal trends in biodiversity and ecosystems or to test ecological hypotheses at larger scales.     

Are We Predicting the Actual or Apparent Distribution of Temperate Marine Fishes?

Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change--particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km(2) study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions.     

Lessons learnt on the analysis of large sequence data in animal genomics

The ’omics revolution has made a large amount of sequence data available to researchers and the industry. This has had a profound impact in the field of bioinformatics, stimulating unprecedented advancements in this discipline. Mostly, this is usually looked at from the perspective of human ’omics, in particular human genomics. Plant and animal genomics, however, have also been deeply influenced by next‐generation sequencing technologies, with several genomics applications now popular among researchers and the breeding industry. Genomics tends to generate huge amounts of data, and genomic sequence data account for an increasing proportion of big data in biological sciences, due largely to decreasing sequencing and genotyping costs and to large‐scale sequencing and resequencing projects. The analysis of big data poses a challenge to scientists, as data gathering currently takes place at a faster pace than does data processing and analysis, and the associated computational burden is increasingly taxing, making even simple manipulation, visualization and transferring of data a cumbersome operation. The time consumed by the processing and analysing of huge data sets may be at the expense of data quality assessment and critical interpretation. Additionally, when analysing lots of data, something is likely to go awry—the software may crash or stop—and it can be very frustrating to track the error. We herein review the most relevant issues related to tackling these challenges and problems, from the perspective of animal genomics, and provide researchers that lack extensive computing experience with guidelines that will help when processing large genomic data sets.