使用Biotracks 采集植物标本

Biotracks 是一款自然观察类的公众科学应用,目前已经被各类科学调查和自然观察项目广泛使用。该文利用Biotracks 的标本采集项目将野外采集的数据与标本馆的数字馆藏系统连接起来,使用户在手机上记录的信息可以被应用到标本馆的标本数字化中。这种方式不仅提升了数字标本的转录效率,而且从根本上改变了整个标本收集流程中的数据整合方式,使得标本从采集到收藏的各个环节都能获得高质量的效率提升。同时,新的标本收集模式还能自然地将标本的野外照片与数字标本融为一体,从而使得传统标本原本很难呈现的颜色、行为、立体结构、环境等信息最终可以通过数字标本再次展现给研究者。这在信息维度上不仅拓展了传统标本的内涵,结合公众科学,未来还有望进一步延伸馆藏标本鉴定和讨论的时空范围。此外,公众科学在解决标本馆问题中所展现出来的潜质,为重新审视标本馆的领域价值提供了新的视角。     

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.     

Desolation comes from the sky: Invasive Hymenoptera species as prey of Chilean giant robber flies (Diptera: Asilidae) through field observations and citizen science

Robber flies (Asilidae) are the main predatory fly family feeding on beetles, butterflies, other flies for true flies, and even spiders; however, Hymenoptera is the most common prey. Invasive Hymenoptera species are common in central and southern Chile; however, few predators of these are known. The hunting behavior and prey of Chilean robber fly species are also poorly known. The aim of this study is to provide the first hunting behavior records of five Chilean giant robber fly species on invasive Hymenoptera. In addition, an updated distribution of these species is provided. Records of hunting behavior were based on fieldwork collections and citizen science observations. The historical distribution was compared with citizen science observations using chi-square analyzes. Twelve predation events were recorded. Obelophorus terebratus was the most common predator. Bombus terrestris was the invasive Hymenoptera most preyed upon. Both the extension of occurrence of Lycomya germainii as Obelophorus species showed changes in his distribution. Only O. landbecki shown changes in area of occupancy. Citizen science is playing a key role in the knowledge of biological interactions and distribution of endemic and native Chilean robber fly species.     

Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway

Many publications make use of opportunistic data, such as citizen science observation data, to infer large‐scale properties of species’ distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them. We first examined the extent of the biases in opportunistic citizen science observations of three wild ungulate species in Norway by comparing them to data from GPS telemetry. We then quantified the extent of the biases by specifying a model of the biases. From the bias model, we sampled available locations within the species’ home range. Along with opportunistic observations, we used the corrected availability locations to estimate a resource selection function (RSF). We tested this method with simulations and empirical datasets for the three species. We compared the results of our correction method to RSFs obtained using opportunistic observations without correction and to RSFs using GPS‐telemetry data. Finally, we compared habitat suitability maps obtained using each of these models. Opportunistic observations are more affected by human access and visibility than locations derived from GPS telemetry. This has consequences for drawing inferences about species’ ecology. Models naïvely using opportunistic observations in habitat‐use studies can result in spurious inferences. However, sampling availability locations based on the spatial biases in opportunistic data improves the estimation of the species’ RSFs and predicted habitat suitability maps in some cases. This study highlights the challenges and opportunities of using opportunistic observations in habitat‐use studies. While our method is not foolproof it is a first step toward unlocking the potential of opportunistic citizen science data for habitat‐use studies.     

Chimpanzee identification and social network construction through an online citizen science platform

Citizen science has grown rapidly in popularity in recent years due to its potential to educate and engage the public while providing a means to address a myriad of scientific questions. However, the rise in popularity of citizen science has also been accompanied by concerns about the quality of data emerging from citizen science research projects. We assessed data quality in the online citizen scientist platform Chimp&See, which hosts camera trap videos of chimpanzees (Pan troglodytes) and other species across Equatorial Africa. In particular, we compared detection and identification of individual chimpanzees by citizen scientists with that of experts with years of experience studying those chimpanzees. We found that citizen scientists typically detected the same number of individual chimpanzees as experts, but assigned far fewer identifications (IDs) to those individuals. Those IDs assigned, however, were nearly always in agreement with the IDs provided by experts. We applied the data sets of citizen scientists and experts by constructing social networks from each. We found that both social networks were relatively robust and shared a similar structure, as well as having positively correlated individual network positions. Our findings demonstrate that, although citizen scientists produced a smaller data set based on fewer confirmed IDs, the data strongly reflect expert classifications and can be used for meaningful assessments of group structure and dynamics. This approach expands opportunities for social research and conservation monitoring in great apes and many other individually identifiable species.     

Equivalence of citizen science and scientific data for modelling species distribution of birds from a tropical savanna

The Wallacean deficit continues to be a challenge to species distribution modelling. Although some authors have suggested that data collected by citizen scientists can be relevant for a better understanding of biodiversity, to our knowledge, no work has quantitatively tested the equivalence between scientific and citizen science data. Here, we investigate the hypothesis that data collected by citizen scientists can be equivalent to data collected by professional scientists when generating species spatial distribution models. For 42 bird species in the Cerrado region we generated and compared species distribution models based on three data sources: (1) scientific data, (2) citizen science data and (3) sample size corrected citizen science data. To test our hypothesis, we compared the equivalence of these datasets. We rejected the hypothesis of equivalence for about one-third (38%) of the evaluated species, revealing that, for most of the species considered, the models generated were equivalent irrespective of the data set used. The distances between centroids of the models that were equivalent were on average smaller than the distances between non-equivalent models. Also, the direction of change in the models showed no pattern, with no trend towards more populated regions. Our results show that the use of data collected by citizen scientists can be an ally in filling the Wallacean deficit gap. In fact, the lack of use of this wide range of data collected by citizen scientists seems to be an unjustified caution. We indicate the potential of using citizen science data for modelling the distribution of species, mainly due to the large set of data collected, which is impracticable for scientists alone to collect. Conservation measures will be favoured by the union of professional and amateur data, aiming for a better understanding of species distribution and, consequently, biodiversity conservation.     

Butterflies & wild bees: biology teachers’ PCK development through citizen science*

Citizen science is a rapidly growing emerging field in science and it is gaining importance in education. Therefore, this study was conducted to document the pedagogical content knowledge (PCK) of biology teachers who participated in a citizen science project involving observation of wild bees and identification of butterflies. In this paper, knowledge about how these biological methods can be taught to students is presented. After two years in the project, four teachers were interviewed and their PCK was captured in the form of content representations (CoRes) and Pedagogical and Professional-Experience Repertoires (PaP-eRs). These results can help future citizen science projects to link their activities to the school curriculum. But not only success can be reported: although one of the project team’s aims was to make the Nature of Science accessible to the teachers and students in the course of the project, the teachers did not take this aspect into account. This paper discusses the possible reasons and proposes various strategies for improving citizen science in the context of school biology learning.     

Citizen science in schools: Engaging students in research on urban habitat for pollinators

Citizen science can play an important role in school science education. Citizen science is particularly relevant to addressing current societal environmental sustainability challenges, as it engages the students directly with environmental science and gives students an understanding of the scientific process. In addition, it allows students to observe local representations of global challenges. Here, we report a citizen science programme designed to engage school‐age children in real‐world scientific research. The programme used standardized methods deployed across multiple schools through scientist–school partnerships to engage students with an important conservation problem: habitat for pollinator insects in urban environments. Citizen science programmes such as the programme presented here can be used to enhance scientific literacy and skills. Provided key challenges to maintain data quality are met, this approach is a powerful way to contribute valuable citizen science data for understudied, but ecologically important study systems, particularly in urban environments across broad geographical areas.     

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

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

An artificial bird nest experiment in urban environments: Lessons from a school‐based citizen science programme

Maintaining suitable vegetation within urban environments is crucial for wildlife conservation in the face of anthropogenic habitat change. Here, we report on a citizen science project, involving students from seven schools across south‐eastern Australia, that investigated the effectiveness of urban vegetation as habitat for bird nests. The ‘nest concealment hypothesis’ posits that vegetation should obscure the nest from predator detection, thus reducing the likelihood of predation. To test this, participating school‐aged citizen scientists constructed artificial nests, which were placed in garden trees within school grounds and monitored for signs of predation. We found no evidence to support the nest concealment hypothesis, with no relationship between the density of vegetation immediately surrounding a nest and its likelihood of predation (binomial model:  = 1.714, P = 0.190). It was observed that 80% of the nests experienced predation. This aligns with mounting evidence suggesting that other factors, such as olfaction and adult defence, may be more important factors in the protection of bird nests. It is important to note that artificial nests are unreliable, and therefore, the veracity of the overall conclusions is limited. However, in conducting this experiment, we demonstrate the suitability of this method as a school‐based citizen science activity. This study exemplifies that field‐based experiments can used to engage future generations with conservation science.     

Citizen Science as a New Tool in Dog Cognition Research

Family dogs and dog owners offer a potentially powerful way to conduct citizen science to answer questions about animal behavior that are difficult to answer with more conventional approaches. Here we evaluate the quality of the first data on dog cognition collected by citizen scientists using the Dognition.com website. We conducted analyses to understand if data generated by over 500 citizen scientists replicates internally and in comparison to previously published findings. Half of participants participated for free while the other half paid for access. The website provided each participant a temperament questionnaire and instructions on how to conduct a series of ten cognitive tests. Participation required internet access, a dog and some common household items. Participants could record their responses on any PC, tablet or smartphone from anywhere in the world and data were retained on servers. Results from citizen scientists and their dogs replicated a number of previously described phenomena from conventional lab-based research. There was little evidence that citizen scientists manipulated their results. To illustrate the potential uses of relatively large samples of citizen science data, we then used factor analysis to examine individual differences across the cognitive tasks. The data were best explained by multiple factors in support of the hypothesis that nonhumans, including dogs, can evolve multiple cognitive domains that vary independently. This analysis suggests that in the future, citizen scientists will generate useful datasets that test hypotheses and answer questions as a complement to conventional laboratory techniques used to study dog psychology.     

协同分布式实验2.0 (CDE 2.0):生态学野外研究新方法

全球野外实验网络是近年来兴起的生态学实验方法,产生了“协同分布式实验”(CDE)、“分布式协作实验”(DCE)的野外实验网络和英国生物学记录中心(BRC)的野外观察网络等具体方法,但均存在尺度小、周期短或数据偏差问题.野外实验网络的协议设计应秉持实验优于观察测量、数据数量优于数据质量和尺度优于操作的原则.综合上述方法,本研究提出利用公众科学(citizen science)获得大范围、多尺度和长周期的数据,采用环境因子作为协变量对公众参与者的实验数据集进行验证后,将其作为后验概率与作为主观概率的生态学家实验数据集进行比对,以完成数据有效性验证,克服公众参与者数据质量瑕疵,并提出了相应的统计学模型.讨论了生态学实验数据中应用先验概率、先验与后验概率的逻辑关系以及后验概率发现演化过程新关系的可能性.这种方法更符合统计学采样规范,并增加了大尺度时空的样本量.该方法有望为生态学研究所寻找的一般性理论提供方法支持,新方法可称之为“协同分布式实验2.0”(CDE 2.0).     

The role of social media in compensating for the lack of field studies: Five new fish species for Mediterranean Egypt

In the Mediterranean Sea, where biological invasions constitute a serious threat, the combination of citizen science and social networks amplified the power of proper field studies, recording species that would have otherwise presumably passed unnoticed. Based on data collected on several Facebook groups, we hereby first report the presence of five fish taxa (Kyphosus sp., Heniochus intermedius, Pomacanthus imperator, Pomacanthus maculosus and Abudefduf sp.) new for the Mediterranean Egypt, revise their distribution in the Mediterranean Sea and discuss their possible introduction pathways. Finally, we provide some considerations on the potentiality of social media for citizen science projects.     

Citizen science needs a name change

Amidst attention towards improving equality, inclusivity, and diversity, citizen science is woefully anachronistic in its name. There is a critical need for this field to distance itself from the exclusionary nature of the term ‘citizen’. We provide reasoning for abandoning this term and an outline for adopting a new name.     

feedr and animalnexus.ca: A paired R package and user‐friendly Web application for transforming and visualizing animal movement data from static stations

Radio frequency identification (RFID) provides a simple and inexpensive approach for examining the movements of tagged animals, which can provide information on species behavior and ecology, such as habitat/resource use and social interactions. In addition, tracking animal movements is appealing to naturalists, citizen scientists, and the general public and thus represents a tool for public engagement in science and science education. Although a useful tool, the large amount of data collected using RFID may quickly become overwhelming. Here, we present an R package (feedr) we have developed for loading, transforming, and visualizing time‐stamped, georeferenced data, such as RFID data collected from static logger stations. Using our package, data can be transformed from raw RFID data to visits, presence (regular detections by a logger over time), movements between loggers, displacements, and activity patterns. In addition, we provide several conversion functions to allow users to format data for use in functions from other complementary R packages. Data can also be visualized through static or interactive maps or as animations over time. To increase accessibility, data can be transformed and visualized either through R directly, or through the companion site: http://animalnexus.ca , an online, user‐friendly, R‐based Shiny Web application. This system can be used by professional and citizen scientists alike to view and study animal movements. We have designed this package to be flexible and to be able to handle data collected from other stationary sources (e.g., hair traps, static very high frequency (VHF) telemetry loggers, observations of marked individuals in colonies or staging sites), and we hope this framework will become a meeting point for science, education, and community awareness of the movements of animals. We aim to inspire citizen engagement while simultaneously enabling robust scientific analysis.     

A continental measure of urbanness predicts avian response to local urbanization

Understanding species-specific relationships with their environment is essential for ecology, biogeography and conservation biology. Moreover, understanding how these relationships change with spatial scale is critical to mitigating potential threats to biodiversity. But methods which measure inter-specific variation in response to environmental parameters that are also generalizable across multiple spatial scales are scarce. We used broad-scale avian citizen science data, over continental Australia, integrated with remotely-sensed products, to produce a measure of urban-tolerance for a given species at a continental-scale. We then compared these urban-tolerances to modelled responses to urbanization at a local-scale, based on systematic sampling within four small cities. For 49 species which had sufficient data for modelling, we found a significant relationship (R² = 0.51) between continental-scale urbanness and local-scale urbanness. We also found that relatively few citizen science observations (~250) are necessary for reliable estimates of continental-scale species-specific urban scores to predict local-scale response to urbanization. Our approach demonstrates the applicability of broad-scale citizen science data, contrasting both the spatial grain and extent of standard point-count surveys generally only conducted at small spatial scales. Continental-scale responses in Australia are representative of small-scale responses to urbanization among four small cities in Australia, suggesting that our method of producing species-specific urban scores is robust and may be generalized to other locations lacking appropriate data.     

Grass Gazers: Using citizen science as a tool to facilitate practical and online science learning for secondary school students during the COVID‐19 lockdown

The coronavirus disease of 2019 (COVID‐19) pandemic has impacted educational systems worldwide during 2020, including primary and secondary schooling. To enable students of a local secondary school in Brisbane, Queensland, to continue with their practical agricultural science learning and facilitate online learning, a “Grass Gazers” citizen science scoping project was designed and rapidly implemented as a collaboration between the school and a multidisciplinary university research group focused on pollen allergy. Here, we reflect on the process of developing and implementing this project from the perspective of the school and the university. A learning package including modules on pollen identification, tracking grass species, measuring field greenness, using a citizen science data entry platform, forensic palynology, as well as video guides, risk assessment and feedback forms were generated. Junior agriculture science students participated in the learning via online lessons and independent data collection in their own local neighborhood and/or school grounds situated within urban environments. The university research group and school coordinator, operating in their own distributed work environments, had to develop, source, adopt, and/or adapt material rapidly to meet the unique requirements of the project. The experience allowed two‐way knowledge exchange between the secondary and tertiary education sectors. Participating students were introduced to real‐world research and were able to engage in outdoor learning during a time when online, indoor, desk‐based learning dominated their studies. The unique context of restrictions imposed by the social isolation policies, as well as government Public Health and Department of Education directives, allowed the team to respond by adapting teaching and research activity to develop and trial learning modules and citizen science tools. The project provided a focus to motivate and connect teachers, academic staff, and school students during a difficult circumstance. Extension of this citizen project for the purposes of research and secondary school learning has the potential to offer ongoing benefits for grassland ecology data acquisition and student exposure to real‐world science.     

Counting Cats: The integration of expert and citizen science data for unbiased inference of population abundance

Free‐roaming animal populations are hard to count, and professional experts are a limited resource. There is vast untapped potential in the data collected by nonprofessional scientists who volunteer their time to population monitoring, but citizen science (CS) raises concerns around data quality and biases. A particular concern in abundance modeling is the presence of false positives that can occur due to misidentification of nontarget species. Here, we introduce Integrated Abundance Models (IAMs) that integrate citizen and expert data to allow robust inference of population abundance meanwhile accounting for biases caused by misidentification. We used simulation experiments to confirm that IAMs successfully remove the inflation of abundance estimates caused by false‐positive detections and can provide accurate estimates of both bias and abundance. We illustrate the approach with a case study on unowned domestic cats, which are commonly confused with owned, and infer their abundance by analyzing a combination of CS data and expert data. Our case study finds that relying on CS data alone, either through simple summation or via traditional modeling approaches, can vastly inflate abundance estimates. IAMs provide an adaptable framework, increasing the opportunity for further development of the approach, tailoring to specific systems and robust use of CS data.     

我国实验动物科学带来的动物伦理及福利问题

实验动物是在一定条件下人工饲养繁殖,具有特定的生物学特性,用于科学研究的动物。实验动物是生命科学研究不可缺少的支撑条件,是为人类的健康和发展作出贡献和牺牲的生命体。实验动物科学包括实验动物和动物实验两个方面,是生命科学的重要分支。随着人类社会、经济和文化的发展,动物福利和动物伦理问题已经全面渗透到了实验动物科学乃至生命科学的各个领域之中。在我国,实验动物科学带来的伦理问题主要表现为：缺乏相应的法律法规,对虐杀实验动物的现象缺乏管理,违背实验动物伦理的技术操作现象依然存在,动物保护主义者和实验动物科学工作者之间的矛盾,等等。动物伦理学在中国出现的社会原因是：改革开放后经济的快速发展促使人们的观念转变,与国际的交往促进了东西方文化的交融,同时也是近十几年来教育的结果。近年来,我国实验动物科学领域内关于伦理问题和动物福利的研究进展迅速,实验动物纪念碑的出现和＂3R＂研究概念的传入是其主要表现。改善实验动物的生活条件,杜绝虐杀实验动物的现象,规范动物实验的技术操作,尽快制定和完善相关的法律法规,是进一步努力的目标;而落实组织措施,成立动物伦理委员会及专家组,加强与国际上的交流,推动＂3R＂研究的进展,扩大对社会的宣传,支持爱护动物的行为,注重与动物保护组织的理解及沟通等,是为达到这一目标应采取的措施。     

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.