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"citizen science"
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Benefits and Drawbacks of Citizen Science to Complement Traditional Data Gathering Approaches for Medically Important Hard Ticks (Acari: Ixodidae) in the United States
Tick-borne diseases are increasing in North America. Knowledge of which tick species and associated human pathogens are present locally can inform the public and medical community about the acarological risk for tick bites and tick-borne infections. Citizen science (also called community-based monitoring, volunteer monitoring, or participatory science) is emerging as a potential approach to complement traditional tick record data gathering where all aspects of the work is done by researchers or public health professionals. One key question is how citizen science can best be used to generate high-quality data to fill knowledge gaps that are difficult to address using traditional data gathering approaches. Citizen science is particularly useful to generate information on human–tick encounters and may also contribute to geographical tick records to help define species distributions across large areas. Previous citizen science projects have utilized three distinct tick record data gathering methods including submission of: 1) physical tick specimens for identification by professional entomologists, 2) digital images of ticks for identification by professional entomologists, and 3) data where the tick species and life stage were identified by the citizen scientist. We explore the benefits and drawbacks of citizen science, relative to the traditional scientific approach, to generate data on tick records, with special emphasis on data quality for species identification and tick encounter locations. We recognize the value of citizen science to tick research but caution that the generated information must be interpreted cautiously with data quality limitations firmly in mind to avoid misleading conclusions.
Journal Article
Three Frontiers for the Future of Biodiversity Research Using Citizen Science Data
Citizen science is fundamentally shifting the future of biodiversity research. But although citizen science observations are contributing an increasingly large proportion of biodiversity data, they only feature in a relatively small percentage of research papers on biodiversity. We provide our perspective on three frontiers of citizen science research, areas that we feel to date have had minimal scientific exploration but that we believe deserve greater attention as they present substantial opportunities for the future of biodiversity research: sampling the undersampled, capitalizing on citizen science’s unique ability to sample poorly sampled taxa and regions of the world, reducing taxonomic and spatial biases in global biodiversity data sets; estimating abundance and density in space and time, develop techniques to derive taxon-specific densities from presence or absence and presence-only data; and capitalizing on secondary data collection, moving beyond data on the occurrence of single species and gain further understanding of ecological interactions among species or habitats. The contribution of citizen science to understanding the important biodiversity questions of our time should be more fully realized.
Journal Article
Optimizing future biodiversity sampling by citizen scientists
We are currently in the midst of Earth's sixth extinction event, and measuring biodiversity trends in space and time is essential for prioritizing limited resources for conservation. At the same time, the scope of the necessary biodiversity monitoring is overwhelming funding for professional scientific monitoring. In response, scientists are increasingly using citizen science data to monitor biodiversity. But citizen science data are ‘noisy’, with redundancies and gaps arising from unstructured human behaviours in space and time. We ask whether the information content of these data can be maximized for the express purpose of trend estimation. We develop and execute a novel framework which assigns every citizen science sampling event a marginal value, derived from the importance of an observation to our understanding of overall population trends. We then make this framework predictive, estimating the expected marginal value of future biodiversity observations. We find that past observations are useful in forecasting where high-value observations will occur in the future. Interestingly, we find high value in both ‘hotspots’, which are frequently sampled locations, and ‘coldspots’, which are areas far from recent sampling, suggesting that an optimal sampling regime balances ‘hotspot’ sampling with a spread across the landscape.
Journal Article
Community biodiversity management : promoting resilience and the conservation of plant genetic resources
\"The conservation and sustainable use of biodiversity in the environments where this diversity originated or is being used, are issues which are high on the policy agenda. This book is the first to set out a clear overview of community biodiversity management (CBM) as an approach to meet social, economic and environmental change\"-- Provided by publisher.
Book
Improving big citizen science data: Moving beyond haphazard sampling
Citizen science is mainstream: millions of people contribute data to a growing array of citizen science projects annually, forming massive datasets that will drive research for years to come. Many citizen science projects implement a \"leaderboard\" framework, ranking the contributions based on number of records or species, encouraging further participation. But is every data point equally \"valuable?\" Citizen scientists collect data with distinct spatial and temporal biases, leading to unfortunate gaps and redundancies, which create statistical and informational problems for downstream analyses. Up to this point, the haphazard structure of the data has been seen as an unfortunate but unchangeable aspect of citizen science data. However, we argue here that this issue can actually be addressed: we provide a very simple, tractable framework that could be adapted by broadscale citizen science projects to allow citizen scientists to optimize the marginal value of their efforts, increasing the overall collective knowledge.
Journal Article
Innovations in urban climate governance : voluntary programs for low-carbon buildings and cities
\"Building on unique data, this book analyses the efficacy of a prominent climate change mitigation strategy: voluntary programs for sustainable buildings and cities. It evaluates the performance of thirty-five voluntary programs from the global north and south, including certification programs, knowledge networks, and novel forms of financing. The author examines them through the lens of club theory, urban transformation theory, and diffusion of innovations theory. Using qualitative comparative analysis (QCA) the book points out the opportunities and constraints of voluntary programs for decarbonising the built environment, and argues for a transformation of their use in climate change mitigation. The book will appeal to readers interested in sustainable city planning, climate change mitigation, and voluntarism as an alternative governance mechanism for achieving socially and environmentally desirable outcomes. The wide diversity of cases from the global north and south generate new insights, and offers practical guidelines for designing effective programs\"-- Provided by publisher.
Book
Integrating citizen-science and planned-survey data improves species distribution estimates
Aim
Mapping species distributions is a crucial but challenging requirement of wildlife management. The frequent need to sample vast expanses of potential habitat increases the cost of planned surveys and rewards accumulation of opportunistic observations. In this paper, we integrate planned‐survey data from roost counts with opportunistic samples from eBird, WikiAves and Xeno‐canto citizen‐science platforms to map the geographic range of the endangered Vinaceous‐breasted Parrot. We demonstrate the estimation and mapping of species occurrence based on data integration while accounting for specifics of each dataset, including observation technique and uncertainty about the observations.
Location
Argentina, Brazil and Paraguay.
Methods
Our analysis illustrates (a) the incorporation of sampling effort, spatial autocorrelation and site covariates in a joint‐likelihood, hierarchical, data integration model; (b) the evaluation of the contribution of each dataset, as well as the contribution of effort covariates, spatial autocorrelation and site covariates to the predictive ability of fitted models using a cross‐validation approach; and (c) how spatial representation of the latent occupancy state (i.e. realized occupancy) helps identify areas with high uncertainty that should be prioritized in future fieldwork.
Results
We estimate a Vinaceous‐breasted Parrot geographic range of 434,670 km2, which is three times larger than the “Extant” area previously reported in the IUCN Red List. The exclusion of one dataset at a time from the analyses always resulted in worse predictions by the models of truncated data than by the Full Model, which included all datasets. Likewise, exclusion of spatial autocorrelation, site covariates or sampling effort resulted in worse predictions.
Main conclusions
The integration of different datasets into one joint‐likelihood model produced a more reliable representation of the species range than any individual dataset taken on its own, improving the use of citizen‐science data in combination with planned‐survey results.
Journal Article