Exploring citizen science applications for wildlife monitoring

Abstract

Citizen science has recently emerged as a powerful tool for wildlife monitoring and conservation efforts, offering cost-effective, large-scale data collection that supports traditional scientific research methods. This study explores the integration of citizen science in wildlife monitoring, citizen science data applications and challenges that could be faced, while giving insights on future directions. Citizen- generated data – including observational, environmental, geospatial, acoustic, photographic and genetic information – contribute to population estimations, health and biodiversity assessments, and threat detection. Moreover, the fusion of advanced technologies including mobile applications, web platforms, remote sensing and artificial intelligence has significantly improved data quality, citizen engagement and geographical coverage. However, challenges persist, including concerns about data accuracy, long-term participant engagement and technical knowledge gaps. Addressing these issues through user-friendly technologies and robust training programs is crucial for maximizing the potential of citizen science in wildlife conservation. As global biodiversity faces increasing threats from climate change and deforestation, citizen science offers a vital solution for wildlife monitoring and protection, fostering stronger connections between humans and nature while supporting evidence-based conservation efforts.

Introduction

The concept of citizen science was known in the early twentieth and recorded for its first time in 1989, when it was used to record a large amount of data on the National Audubon Society’s Christmas Bird Count (Baalbaki et al., 2019). Originally, “citizen science” was associated with being a part of a wider process called citizen-generated data processes and practices that incorporate citizens/volunteers in data collection strategies (Haklay et al., 2021). Recently, special attention was given to “citizen science”, as it counts on involving the public and community in science. Herein, citizen science is shown to be associated with various terms such as “community-based research” and “participatory action research”, aiming at involving public health, environmental, and social science research (Strasser et al., 2018). In simple terms, as per the National Park Service, this term clearly emphasizes that science is for everyone. It is used to reflect the public involvement in different stages of scientific research and development, including design experiments, data collection, results analysis, and problem-solving. This incorporation supports scientists, decision-makers, and managers to effectively address scientific problems using local and community-based evidence (National Park Service, 2024). Similarly, citizen science integration can help with capacity building while offering new job opportunities, improving employment, enhancing knowledge among locals, and most importantly creating environmental awareness (Aberasturi Rodríguez et al., 2024).

The World Wide Fund for Nature (WWF) showcases that ecosystem and biodiversity conservation is vital for not only human protection but also climate change mitigation, food security, land protection, and environmental sustainability. This is associated with their significance in food and nutrition provision, disaster reduction, shelters for species and habitats, local community empowerment, and medicine supply (Craig Beatty, 2020). Despite its importance, wildlife and biodiversity are currently at risk of various threats and challenges including climate change impacts, habitat destruction and fragmentation, illegal trade and poaching, increased pollution, and invasive species diffusion (Gupta et al., 2023). This underscores the crucial need for wildlife monitoring, biodiversity management, and ecosystem conservation, which are connected to the sustainable development goals (SDGs) and human health (WHO, 2022).

Wildlife monitoring activities play a fundamental role in achieving efficient management, attaining successful conservation projects and overcoming challenges. Monitoring is used in the initial stages to assess land and ecosystem conditions, identify population size, measure changes, and detect threats. Following that, monitoring measures the success of conservation actions, detects drawbacks and barriers, and guides for better management approaches (Gill and Daltry, 2014). An ongoing monitoring system of wildlife populations and their habitats supports research initiatives, focusing on collecting data on habitat changes, areas, behavior, and needs. These data eventually inform wildlife conservers, stakeholders, and decision-makers to adopt conservation-oriented strategies and properly adjust management approaches to habitat and ecosystem needs (Ward, 2023). However, monitoring can be challenging and time-consuming without clear planning, which can result in a huge amount of complex, inaccurate, and hard-to-analyze data. Hence, it is crucial to develop clear, simple, and replicable monitoring programs, especially seeing the large time needed to experience the results of conservation approaches (Gill and Daltry, 2014).

Citizen science based on community and local involvement is seen as an efficient and cost-effective tool to support wildlife monitoring approaches. As previously mentioned, citizen science aims to incorporate citizens mainly in data collection and sampling designs, where data gathered are effectively used to quantify, assess, and evaluate species and population patterns and trends (Sun et al., 2021). Citizens’ integration into scientific research provides large-scale, cost-effective, and accurate monitoring programs of wildlife populations and their related species. This is mainly linked to their capacity to support field- based, temporal, and spatial data collection. Herein, the combination of citizen data and other datasets and data types significantly improves wildlife population and species estimation, assists in the density and distribution establishment of several wild animals, and eventually supports wildlife monitoring and informing conservation (Sgroi et al., 2023).

Citizens mainly contribute to field-based data collection by conducting field surveys which have proved to be a cost-effective method to offer insight into different habitats’ trends and patterns (Razen et al., 2020). However, an increased interest in novel technologies such as remote sensing, machine learning, and artificial intelligence (AI), highlights the potential integration of these technologies with citizen science, resulting in more accurate, cost-effective, large-scale, and faster data collection methods. For instance, in Spain, an increase in the use of autonomous recording units (ARUs) for wildlife monitoring shows the citizens’ contribution in assessing and identifying the rate and the most common season/time of the year for bird migration. Citizen science records were mainly used as an independent and accurate source to compare and validate trends in bird migration (Bota et al., 2020). Likewise, in Australia, decision-makers combined between “remotely piloted aircraft (RPA)” also known as “Unmanned aerial vehicles (UAVs)” or “drones” power and citizen science capacity to effectively monitor and estimate seal abundance. RPA was mainly used to collect an orthomosaic image of the entire fur seal breeding area. Citizens were using spatial images to count and record the number of seals using a citizen science online portal that helps citizens divide seals into categories and effectively provide accurate information (Sorrell et al., 2019).

Citizen science not only supports wildlife monitoring but also proves beneficial on multiple levels including increasing research capacity, engaging communities in problems and solutions, improving public awareness, and identifying barriers and challenges from public perspectives (Rowbotham et al., 2023). Moreover, the integration of novel technology (e.g. remote sensing, machine learning, internet of things (IoT), online platforms, AI, etc.) can highly serve as a valuable component for citizen science practices. This integration potentially ensures high levels of performance among citizens, boosts their motivation and immersion, improves data quality and accuracy and eases communication between citizens and stakeholders (Lemmens et al., 2021; Sun et al., 2021; Silva et al., 2022). Likewise, novel technology integration effectively overcomes several challenges including data replicability, small scale monitoring, and lack of accuracy, by offering a cost-effective tool to monitor wildlife on a large scale with more reliable and accurate data (Sorrell et al., 2019).

However, integrating citizen science in a long monitoring process requires continuous capacity building, training, commitment, time, effort, and motivation among participants. These requirements can potentially influence the participation rate, resulting in delays in the process (Walker et al., 2020). Additionally, without a standardized and well-structured adoption of citizen science, data quality and accuracy can be highly influenced requiring continuous monitoring and maintenance (Balázs et al., 2021). This study mainly focuses on providing a better understanding of the integration of citizen science in wildlife monitoring and management. The main objectives aim to i) identify how citizen data is used and collected for wildlife monitoring initiatives, ii) highlight the use and application of the data collected by citizens and iii) determine the challenges associated with its implementation. Lastly, this research intends to motivate and empower citizens to be actively involved in scientific research, biodiversity conservation, and ecosystem protection while boosting their knowledge and skills in wildlife monitoring. Nevertheless, this concept should encourage policy-makers and stakeholders to optimize their resources, expand conservation efforts, and effectively adhere to UN sustainable development goals.

Citizen science data in wildlife monitoring

Types of citizen science data

Citizen science data have been found to mainly focus on species identifications and counts that aim at identifying species type and population dynamics while giving insights into their abundance, density, and appearance. Moreover, this data supports behavioral observations of species and habitats, while assessing conditions and changes. In sum, citizens significantly help with collecting environmental parameters such as species distribution, migration patterns, and seasonal changes (Fontaine et al., 2022). Some of the major types of citizen science data collected are as follows.

Observational data

These data mainly cover population size, distribution, and habitat use. In addition, to data on species presence, absence, abundance, noise, and occurrence probabilities. Herein, data is mainly gathered through citizens’ observations, hikes, and camera traps. Afterwards, the data are submitted to specific platforms and applications such as iNaturalist, eBird, and camera traps. For instance, a platform called iSeeMammals was launched in 2017 to assess the significance of citizen science in improving population abundance estimates. This platform enabled citizens to effectively collect different types of data, which was summarized in “presence-only”, “absence”, or “non-detections” (Sun et al., 2021). In general, observational data informs stakeholders on species presence, locations, behaviors (e.g., feeding, mating, and nesting), and timing of natural events (e.g., migration and breeding) (McKinley et al., 2015).

Environmental data

Citizens contribute to environmental science and protection, through observing and documenting specific environmental data. These data mainly include information on land use land cover change (LULCC), deforestation, water and air quality, and disease threats (Fraisl et al., 2022). In addition, other environmental variables and parameters such as air pollution, noise disturbance, temperature, humidity, rainfall, pH levels, and pollutant concentrations can be gathered to effectively improve the environmental quality of life and enhance sustainable livelihoods and biodiversity management (Alfonso et al., 2022; United States Environmental Protection Agency, 1997).

Geospatial data

Citizens equipped with robust spatial technologies and/or GPS-enabled smartphones, can effectively gather accurate remote sensing data. Different data sources can be generated, including aerial images, thermal images, airborne laser scanner (ALS) data, land-based mobile mapping data, and unmanned aerial vehicle (UAV) data (Leena Matikainen, 2016). Using these sources citizens could support wildlife monitoring by providing insights on natural hazards such as floods, wildfires, and/or landslides, species’ geographical locations, movements, and migration patterns, information on land use land cover changes (LULCC) and environmental parameters such as normalized difference vegetation index (NDVI), soil moisture, and vegetation health. Thus, the integration of citizen science with open earth observation data significantly informs biodiversity efforts, improves wildlife monitoring, and supports disaster management and ecosystem protection (Manfré et al., 2012; Rondon et al., 2023).

Acoustic data

This type of data benefits from the integration of citizens into wildlife monitoring and management. Citizens can use recording devices to measure and record acoustic sounds of animals, ecosystems, and nature (e.g. bird tweeting, frog calls, winds, water flow, etc.). In general, sound and acoustic recordings are significantly used to gather a better understanding of animal biodiversity and to monitor species presence, behavior, and communication methods (Darras et al., 2016). This is achieved through a wide range of platforms and applications allowing an easy and effective record process for citizens. For instance, FrogID, an effective platform and database for frogs, allows citizens to easily identify and record species through smartphone technology. This platform offers a biodiversity database with geo-referenced frog species records and a digital collection of frog calls (Jodi J L Rowley, 2019).

Photographic data

An effective and successful wildlife monitoring process provides visual evidence of species presence, abundance, behaviors, and conditions. Therefore, by using specific platforms (such as iNaturalist and eBird), citizens can provide, upload, and share photographs and videos on species and habitats during their observations. This helps in documenting rare species, identifying individual animals, and recording changes over time. These databases showcase the public engagement with nature and different species (Sara Stoudt et al., 2022).

Genetic data

Genomic data are an effective tool to analyze and predict biodiversity patterns and trends. These data serve as a robust, quantitative, and comparable foundation for biodiversity assessments, conservation, and restoration (Theissinger et al., 2023). Therefore, citizens contribute to this end by collecting physical samples (e.g. hair, feces, etc.) for genetic analysis. Data obtained advise on species’ genetic diversity, endangered species, population structure, and health (Smith & Wang, 2014).

Data collection methods and procedures

Citizens are the main contributors to a comprehensive and large-scale data collection process in citizen science projects. Hence, it is crucial to offer them comprehensive and clear approaches for collecting the required data. Data collection processes vary between two known methods: “traditional field-based methods” and “remote sensed methods”.

Traditional methods used for monitoring generally comprise i) observations and recordings made by humans that can be acoustic and/or visual, ii) transect surveys, and iii) camera trapping (Zwerts et al., 2021). These methods contribute to gathering observational, environmental, acoustic, and genetic data, where it became widely used worldwide. For instance, several organizations such as European Citizen Science Association, the Citizen Science Association in the USA, the Australian Citizen Science Association, and the Citizen Science Global Partnership relied on this method for monitoring projects and initiatives (Fraisl et al., 2022).

However, traditional field-based data collection methods encompass several limitations including limited spatial coverage, especially for inaccessible and large geographical areas. Geospatial technologies proved to be efficient in overcoming these limitations. As a result, geospatial data generated using remote sensing technology including i) satellite imagery such as Landsat and Sentinel, ii) Unmanned Aerial Vehicles, iii) Geographic Information System (GIS), and iv) automated sensor networks such as camera traps and wildlife sensors are widely used nowadays. These technologies serve as a robust tool for citizens to effectively participate in the data collection process, and eventually increase their involvement in scientific research and wildlife monitoring (Sakshi Singh, 2024). Hence, the integration between field data and remote sensed data results in large-scale monitoring, ensures the reliability, accuracy, and validity of collected data, and optimizes efforts and resources efficiently.

Data quality and validation

Citizen science is mainly based on the contribution of the public, local people, and volunteers throughout the process. Therefore, data quality is a primary concern for researchers employing public participation. Herein, project managers and researchers should maintain ongoing data monitoring, verification, and validation to ensure the reliability and accuracy of collected data (Anne M Land, 2021). To achieve this, project leaders can benefit from implementing a standardized data collection process with specific guidelines that reduce variety in documentation and records. Data verification methods also include peer verification, cross-referencing with existing datasets, field validation by experts, and model-based quality assessment (Balázs, Mooney, Nováková, et al., 2021). Lastly, advanced platforms and applications with built-in features such as iNeutralist, eBird, and SMART, work on detecting errors and verifying the field data, eventually maintaining data quality and validity. For instance Ol Pejeta Conservancy adopted the use of smartphone applications to facilitate data collection of wildlife sightings, mortality, human-wildlife conflicts, intrusions, and environmental hazards. The switch from paper documentation to digitalized data collection enhanced the accuracy of their data while reducing errors, missed information, and delayed submissions (Alfred Kibungei, 2023). These efforts ensure that gathered data by all participants contributes to informed decision-making and  effective management  practices.

Data sharing, standardization and privacy

Beyond data validation and quality assurance, citizen science data can also benefit from a standardized geodatabase to improve data sharing and standardization. Global, local, and regional geodatabases are open data sources serving as a vital tool for citizens to document, record, and submit their observations, and for stakeholders and project leaders to receive, compile, and share citizen science findings. This integration is valuable to address data quality issues especially for big data, facilitate data integration and sharing, minimize data loss and replication, and increase efficiency and decrease redundancy between citizens and stakeholders. However, stakeholders should ensure that these geodatabases are suitable for various users’ backgrounds and expertise (Kocaman et al., 2022). Herein, they can highly benefit from the integration of novel tools such as remote sensing, machine learning, and AI, offering a cost-effective and large scale coverage and monitoring.

Nevertheless, involving the public/citizens in science requires careful planning, special attention, and appropriate policies and regulations to maintain data privacy and security. This is critical to ensure the success of the projects, minimize biases, and maintain confidentiality between citizens and project leaders, which will eventually support future citizen science projects and research (Bowser et al., 2014).

Applications of citizen science in wildlife monitoring

In the last decade, citizens’ involvement in science and research has served as a cost-effective and large- scale tool to fill the gap in wildlife monitoring and ultimately contribute to the conservation of rare and threatened species. Citizen science projects provide the opportunity for stakeholders to monitor lands, by combining earth observations and ground-based information, through member volunteers and public participation. The use of citizen science has dramatically increased in different fields including climate change, sustainable development, ecosystem monitoring and characterization, drought, and land cover or land-use change (ARSET, 2023). Some of the most prominent applications are as follows.

Population, density and distribution estimation

Citizen science proved efficient to assess density and distribution of several wild species, in both rural and urban environments (Sgroi et al., 2023). Formalized surveys and online platforms support in gathering population insights on species distribution and trends. For instance, in North America, data gathered by eBird were used and compared with formal breeding bird surveys to assess population trends for 574 bird species. This study shows 54% decline in species based on data collected through formal breeding birds surveys, where only 46% decline was shown using eBird data. Nonetheless, the results highlight that citizen data can be significantly used to approximate species population trends and considered in conservation and management efforts (Horns et al., 2018).

Ecosystem and habitat health assessment

Additionally, citizens play a vital role in assessing the health, stability, and quality of various habitats and ecosystems. This is associated with their ability to gather data on specific functions and structures of each habitat, while giving insights on future threats and pressures related to each ecosystem (Kallimanis et al., 2017). For instance, in Lebanon, a collaborative citizen science approach was used to assess the groundwater quality in a Lebanese village. This approach involves local citizens and university scientists who are asked to fill information related to underground water quality using an online platform. This integration marked successful underground water quality and ecosystem health assessment and monitoring (Baalbaki et al., 2019).

Challenges and threats detection

Citizen science initiatives have proven valuable in detecting and monitoring various challenges and threats to wildlife and ecosystems. Participants report observations of invasive species, pollution incidents, habitat degradation, or unusual wildlife behavior that may indicate emerging threats. The NatureWatch community in Canada invented a PlantWatch program to engage all Canadians in collecting scientific information on invasive plant species, which helped researchers track their spread and impact on human health and the environment (NatureWatch, 2024). These efforts facilitate rapid response to environmental threats, optimize resources and inform the best conservation strategies.

Informative tool

Many conservation measures used citizen science data to inform management and mitigation plans, modification of threat status, identification of protected areas, habitat restoration, control of invasive species, captive breeding programs and awareness campaigns (Fontaine et al., 2022). In general, this offers a robust and evidence-based habitat management and conservation, where decision-makers should benefit from citizen science data to efficiently plan future conservation projects and policies. Moreover, this helps build local capacity and knowledge among local communities which should be responsible for continuous monitoring of habitats and ecosystems (Baalbaki et al., 2019). The following figure (Figure 2) summarizes the main applications of citizen science data in various aspects of wildlife conservation and monitoring.

Challenges and limitations

Citizen science implementation faces several challenges and limitations if not properly integrated into wildlife monitoring. These challenges vary between:

• Data accuracy and reliability: Many monitoring approaches adopted citizen science to gather insight on species and environment. However, using these data rely on ensuring data accuracy and reliability. Several challenges can affect data accuracy, including choosing wrong sampling locations, documenting similar observations, and recording incomplete information. Consequently, this will result in the identification of less structures, functions, types, threats, and pressures, thus altering the effectiveness of conservation practices (Kallimanis et al., 2017).
• Lack of technical knowledge: Individuals knowledge, expertise, and potential vary between different participants. Despite the efficiency of novel technologies, citizens may face several difficulties that can affect their contribution to the thinking, design, and outcomes of projects (Grigoletto et al., 2023).
• Long-term engagement: Wildlife monitoring projects often require a commitment for a long period to effectively assess and detect changes. This can influence the capacity of citizens, especially volunteers, to deal with large time commitment and participation (Fraisl et al., 2022).
• Funds and financing: Funding being a primary concern that can limit the scope and sustainability of projects. The financial constraints in citizen science projects can impact the ability to foster effective working partnerships between scientists and citizens, potentially hindering the full realization of the approach’s benefits. Securing adequate funding is crucial to ensure that all stakeholders, including both scientists and citizen participants, receive sufficient benefits from the citizen science process, which is essential for the long-term success of such initiatives (Gunnel et al., 2021).
• Pandemics and lock-down: Pandemics (e.g. COVID-19) significantly affect citizen science by reducing the rate of citizen participation in monitoring and management approaches. Safety and health restrictions potentially influence traditional data collection approaches, ultimately minimizing the numbers of citizens’ observations, coverage of areas, and geographical distribution (Stenhouse et al., 2022).

Future directions and potential solutions

Advanced technology serves as an invaluable tool to support wildlife monitoring processes and applications. This invaluable tool holds potential to overcome challenges associated with citizen science while boosting their performance and outcomes. These technologies ensure an easy and effective data collection process for public participants.

Recently, different web platforms and mobile applications (such as iNeutralist, eBird, SealSpotter, SMART, etc.) have been introduced to be efficient for recording, documenting, sharing, and visualizing habitats and species. Likewise, satellite imagery with its wide range of data sources can be significantly integrated to offer cost-effective and wide coverage assessment. For instance, SealSpotter, an open-access citizen science data portal was developed to support participants through their observations and seal monitoring, by screening remotely piloted aircraft images for animals from different categories and types (Sorrell et al., 2019). Nevertheless, artificial intelligence (AI) and machine learning (ML) play an important role in inventing user-friendly, clear, and straightforward platforms for public utilization. Citizen science applications need to maintain high levels of performance, by offering a clear and comprehensive design and features. These efforts will highly contribute to data quality and accuracy, while increasing user motivation and good performance (Lemmens et al., 2021).

A successful integration of citizen science in wildlife monitoring needs to engage community members in a supportive, meaningful, and standardized manner. To achieve this, project leaders should offer well- structured training programs to equipped citizens with acquired skills and knowledge. Ensuring a continuous capacity building for participants throughout the process will offer more reliable outcomes and facilitate achieving projects goals. This will encourage and motivate citizens to build better relationships with nature and to contribute more to conservational programs. For instance, the Global Learning and Observations to Benefit the Environment (GLOBE) – an international science and education program – coupled with a citizen science app, called GLOBE Observer, were efficiently adopted by volunteers to document observations and contribute to the community (ARSET, 2023). Herein, younger generations who have an advanced knowledge in online platforms and web applications, can markedly increase participation and data quality by facilitating data collection process without the need for detailed and long training (Sun et al., 2021).

Concluding remarks

This paper aimed to provide a comprehensive understanding on the integration of citizen science within wildlife monitoring and management efforts. Findings show that the use of citizen science has dramatically increased in the past decade, offering cost-effective and large-scale data collection aligned with traditional scientific research methods. Citizen science data including multiple types (i.e. observational, environmental, geospatial, acoustic, photographic, and genetic), offer a meticulous assessment of habitats, populations, and ecosystem health. Advanced technologies (such as mobile apps, web platforms, remote sensing, and artificial intelligence) greatly contribute to citizen science projects by ensuring data quality, empowering citizens, expanding geographical coverage, and enhancing participants’ capabilities. Nevertheless, citizen science faces various challenges and limitations related to data accuracy, long-term engagement, technical knowledge, and pandemics. These challenges can be thoroughly addressed through the integration of user-friendly technologies and by ensuring a robust and comprehensive training program and capacity building approaches.

Citizen science can greatly boost the relationship between human and nature, help identifying threatened species, and lead to better management and sustainable conservational efforts. Stakeholders and project leads would highly benefit from citizen science data by building evidence-based projects and initiatives. Herein, as global biodiversity is currently facing increased threats, citizen science can be a vital solution for wildlife monitoring and protection. Further research can gather a deeper insight into long-term impacts of citizen science projects, focusing on defining the best practices and exploring new potential applications.

Acknowledgments

The authors are thankful to the following researchers for their assistance during the manuscript revision stage: Abhilash Dutta Roy, Jorge Montenegro and the Ecoresolve team.

References

1. Aberasturi Rodríguez, A., Bandera, I. F., & Navarro-Pedreño, J. (2024). Can Citizens Do Science? Science in Common and Social Responsibility. Sci, 6(2), 26. https://doi.org/10.3390/sci6020026
https://doi.org/10.3390/sci6020026
 
2. Alfonso, L., Gharesifard, M., & Wehn, U. (2022). Analysing the value of environmental citizen- generated data: Complementarity and cost per observation. Journal of Environmental Management, 303, 114157. https://doi.org/10.1016/j.jenvman.2021.114157
https://doi.org/10.1016/j.jenvman.2021.114157
 
3. Alfred Kibungei. (2023). Digitising Data Collection for Enhanced Wildlife Management | PANORAMA. https://panorama.solutions/en/solution/digitising-data-collection-enhanced- wildlife-management
 
4. Anne Bowser, Lea Shanley, & Andrea Wiggins. (2014). Sharing data while protecting privacy in citizen science. https://www.researchgate.net/publication/261959198_Sharing_data_while_protecting_privacy_in
https://doi.org/10.1145/2540032
 
_citizen_science
 
5. Anne M Land. (2021). Participants in Citizen Science. https://www.researchgate.net/publication/347512452_Participants_in_Citizen_Science
 
6. ARSET. (2023, January 24). ARSET – Connecting Citizen Science with Remote Sensing | NASA Applied Sciences. http://appliedsciences.nasa.gov/get-involved/training/english/arset-connecting-citizen- science-remote-sensing
 
7. Baalbaki, R., Ahmad, S. H., Kays, W., Talhouk, S. N., Saliba, N. A., & Al-Hindi, M. (2019). Citizen science in Lebanon-A case study for groundwater quality monitoring. Royal Society Open Science, 6(2), 181871. https://doi.org/10.1098/rsos.181871
https://doi.org/10.1098/rsos.181871
 
8. Balázs, B., Mooney, P., Novakova, E., Bastin, L., & Jokar Arsanjani, J. (2021). Data Quality in Citizen Science (pp. 139-157). https://doi.org/10.1007/978-3-030-58278-4_8
https://doi.org/10.1007/978-3-030-58278-4_8
 
9. Balázs, B., Mooney, P., Nováková, E., Bastin, L., & Jokar Arsanjani, J. (2021). Data Quality in Citizen Science. In K. Vohland, A. Land-Zandstra, L. Ceccaroni, R. Lemmens, J. Perelló, M. Ponti,
https://doi.org/10.1007/978-3-030-58278-4_8
 
R. Samson, & K. Wagenknecht (Eds.), The Science of Citizen Science (pp. 139-157). Springer International Publishing. https://doi.org/10.1007/978-3-030-58278-4_8
https://doi.org/10.1007/978-3-030-58278-4_8
 
10. Bota, G., Traba, J., Sardà-Palomera, F., Giralt, D., & Pérez Granados, C. (2020). Acoustic Monitoring of Diurnally Migrating European Bee-Eaters Agrees with Data Derived from Citizen Science. Ardea -Wageningen-, 108. https://doi.org/10.5253/arde.v108i2.a3
https://doi.org/10.5253/arde.v108i2.a3
 
11. Gill. D., & Daltry J., (2014). https://www.bgci.org/wp/wp-content/uploads/2023/02/Brief-3- English.pdf
 
12. Craig Beatty. (2020). How Habitat Conservation and Restoration Support Better Human Health Outcomes. World Wildlife Fund. https://www.worldwildlife.org/blogs/sustainability- works/posts/how-habitat-conservation-and-restoration-support-better-human-health-outcomes
 
13. Darras, K., Pütz, P., Fahrurrozi, Rembold, K., & Tscharntke, T. (2016). Measuring sound detection spaces for acoustic animal sampling and monitoring. Biological Conservation, 201. https://doi.org/10.1016/j.biocon.2016.06.021
https://doi.org/10.1016/j.biocon.2016.06.021
 
14. Fontaine, A., Simard, A., Brunet, N., & Elliott, K. (2022). Scientific contributions of citizen science applied to rare or threatened animals. Conservation Biology, 36. https://doi.org/10.1111/cobi.13976
https://doi.org/10.1111/cobi.13976
 
15. Fraisl, D., Hager, G., Bedessem, B., Gold, M., Hsing, P.-Y., Danielsen, F., Hitchcock, C. B., Hulbert,
 
J. M., Piera, J., Spiers, H., Thiel, M., & Haklay, M. (2022). Citizen science in environmental and ecological sciences. Nature Reviews Methods Primers, 2(1), 1-20. https://doi.org/10.1038/s43586-022- 00144-4
https://doi.org/10.1038/s43586-022-00144-4
 
16. Grigoletto, F., de Oliveira, F. A., Momesso, C. C., Nehemy, I. K. R., Junior, J. E. de A., São Pedro,
 
V. de A., Greco, R., Alves, M. A., & Edwards, T. (2023). Technological Affordance and the Realities of Citizen Science Projects Developed in Challenging Territories. Sustainability, 15(8), Article 8. https://doi.org/10.3390/su15086654
https://doi.org/10.3390/su15086654
 
17. Gunnell. J., Golumbic. Y., Hayes. T., & Cooper. M. (2021). Co-created citizen science: Challenging cultures and practice in scientific research. https://www.researchgate.net/publication/354874593_Co- created_citizen_science_Challenging_cultures_and_practice_in_scientific_research
https://doi.org/10.22323/2.20050401
 
18. Gupta, S., Kumaresan, P. R., Saxena, A., Mishra, M., Upadhyay, L., Tamilmani, A., Magrey, A., &
 
S., A. (2023). Wildlife Conservation and Management: Challenges and Strategies. UTTAR PRADESH JOURNAL OF ZOOLOGY, 44, 280-286.
https://doi.org/10.56557/upjoz/2023/v44i243840
 
https://doi.org/10.56557/UPJOZ/2023/v44i243840
https://doi.org/10.56557/upjoz/2023/v44i243840
 
19. Haklay, M. (Muki), Dörler, D., Heigl, F., Manzoni, M., Hecker, S., & Vohland, K. (2021). What Is Citizen Science? The Challenges of Definition. In K. Vohland, A. Land-Zandstra, L. Ceccaroni, R. Lemmens, J. Perelló, M. Ponti, R. Samson, & K. Wagenknecht (Eds.), The Science of Citizen Science (pp. 13-33). Springer International Publishing. https://doi.org/10.1007/978-3-030-58278-4_2
https://doi.org/10.1007/978-3-030-58278-4_2
 
20. Horns, J. J., Adler, F. R., & Şekercioğlu, Ç. H. (2018). Using opportunistic citizen science data to estimate avian population trends. Biological Conservation, 221, 151-159. https://doi.org/10.1016/j.biocon.2018.02.027
https://doi.org/10.1016/j.biocon.2018.02.027
 
21. Jodi J L Rowley. (2019). FrogID: Citizen scientists provide validated biodiversity data on frogs of Australia | Request PDF. https://www.researchgate.net/publication/332876523_FrogID_Citizen_scientists_provide_validate d_biodiversity_data_on_frogs_of_Australia
 
22. Kallimanis, A. S., Panitsa, M., & Dimopoulos, P. (2017). Quality of non-expert citizen science data collected for habitat type conservation status assessment in Natura 2000 protected areas. Scientific Reports, 7, 8873. https://doi.org/10.1038/s41598-017-09316-9
https://doi.org/10.1038/s41598-017-09316-9
 
23. Kocaman, S., Saran, S., Durmaz, M., Kumar, A. S., Durmaz, M., & Kumar, A. S. (Eds.). (2022). Citizen Science and Geospatial Capacity Building. MDPI – Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/books978-3-0365-3714-6
https://doi.org/10.3390/books978-3-0365-3714-6
 
24. Leena Matikainen. (2016). Remotely Sensed Data-An overview | ScienceDirect Topics. https://www.sciencedirect.com/topics/computer-science/remotely-sensed-data
 
25. Lemmens, R., Antoniou, V., Hummer, P., & Potsiou, C. (2021). Citizen Science in the Digital World of Apps (pp. 461-474). https://doi.org/10.1007/978-3-030-58278-4_23
https://doi.org/10.1007/978-3-030-58278-4_23
 
26. Manfré, L. A., Hirata, E., Silva, J. B., Shinohara, E. J., Giannotti, M. A., Larocca, A. P. C., & Quintanilha, J. A. (2012). An Analysis of Geospatial Technologies for Risk and Natural Disaster Management. ISPRS International Journal of Geo-Information, 1(2), Article 2. https://doi.org/10.3390/ijgi1020166
https://doi.org/10.3390/ijgi1020166
 
27. McKinley, D., Miller-Rushing, A., Ballard, H., Bonney, R., Brown, H., Evans, D., French, R., Parrish, J., Phillips, T., Ryan, S., Shanley, L., Shirk, J., Stepenuck, K., Weltzin, J., Wiggins, A., Boyle, O., Briggs, R., Chapin III, F. S., Hewitt, D., & Soukup, M. (2015). Investing in Citizen Science Can Improve Natural Resource Management and Environmental Protection. Issues in Ecology, 2015. https://doi.org/10.1016/j.biocon.2016.05.015
https://doi.org/10.1016/j.biocon.2016.05.015
 
28. National Park Service. (n.d.). What is Citizen Science? – Citizen Science (U.S. National Park Service). Retrieved July 24, 2024, from https://www.nps.gov/subjects/citizenscience/citizen-science.htm
 
29. NatureWatch. (2024). Nature Watch-Canada. PlantWatch. https://www.naturewatch.ca/plantwatch/
 
30. Razen, N., Kuralt, Ž., Flezar, U., Bartol, M., Černe, R., Kos, I., Krofel, M., Luštrik, R., Majic, A., & Potočnik, H. (2020). Citizen science contribution to national wolf population monitoring: What have we learned? European Journal of Wildlife Research, 66. https://doi.org/10.1007/s10344-020- 01383-0
https://doi.org/10.1007/s10344-020-01383-0
 
31. Rondon, M., Ewane, E. B., Abdullah, M. M., Watt, M. S., Blanton, A., Abulibdeh, A., Burt, J. A., Rogers, K., Ali, T., Reef, R., Mohtar, R., Sidik, F., Fahrenberg, M., de-Miguel, S., Galgamuwa, G.
 
A. P., Charabi, Y. A. R., Arachchige, P. S. P., Velasquez-Camacho, L. F., Al-Awadhi, T., … Mohan,
 
M. (2023). Remote sensing-based assessment of mangrove ecosystems in the Gulf Cooperation Council countries: A systematic review. Frontiers in Marine Science, 10, 1241928. https://doi.org/10.3389/fmars.2023.1241928
https://doi.org/10.3389/fmars.2023.1241928
 
32. Rowbotham, S., Walker, P., Marks, L., Irving, M., Smith, B. J., & Laird, Y. (2023). Building capacity for citizen science in health promotion: A collaborative knowledge mobilisation approach. Research Involvement and Engagement, 9, 36. https://doi.org/10.1186/s40900-023-00451-4
https://doi.org/10.1186/s40900-023-00451-4
 
33. Sakshi Singh. (2024). When Citizen Science Meets Geospatial Technologies: Crowdsourcing Solutions for Development – AGI India. https://agiindia.com/when-citizen-science-meets-geospatial- technologies-crowdsourcing-solutions-for-development/
 
34. Sara Stoudt, Benjamin R. Goldstein, & Perry de Valpine. (2022). Identifying engaging bird species and traits with community science observations. https://www.researchgate.net/publication/359922985_Identifying_engaging_bird_species_and_tr aits_with_community_science_observations
https://doi.org/10.1101/2021.06.05.446577
 
35. Sgroi, G., D’Alessio, N., Vada, R., Ferroglio, E., Vicente, J., & Veneziano, V. (2023). The contribution of citizen science in the surveillance of wildlife and related arthropods. Parasitology, 150(12), 1089-1095. https://doi.org/10.1017/S0031182023001038
https://doi.org/10.1017/S0031182023001038
 
36. Silva, C. A., Hudak, A. T., Vierling, L. A., Valbuena, R., Cardil, A., Mohan, M., … & Klauberg, C. (2022). treetop: A Shiny‐based application and R package for extracting forest information from LiDAR data for ecologists and conservationists. Methods in Ecology and Evolution, 13(6), 1164- 1176.
https://doi.org/10.1111/2041-210X.13830
 
37. Smith, O., & Wang, J. (2014). When can noninvasive samples provide sufficient information in conservation genetics studies? Molecular Ecology Resources, 14(5), 1011-1023. https://doi.org/10.1111/1755-0998.12250
https://doi.org/10.1111/1755-0998.12250
 
38. Sorrell, K. J., Clarke, R. H., Holmberg, R., & McIntosh, R. R. (2019). Remotely piloted aircraft improve precision of capture-mark-resight population estimates of Australian fur seals. Ecosphere, 10(8), e02812. https://doi.org/10.1002/ecs2.2812
https://doi.org/10.1002/ecs2.2812
 
39. Stenhouse, A., Perry, T., Grützner, F., Rismiller, P., Koh, L. P., & Lewis, M. (2022). COVID restrictions impact wildlife monitoring in Australia. Biological Conservation, 267, 109470. https://doi.org/10.1016/j.biocon.2022.109470
https://doi.org/10.1016/j.biocon.2022.109470
 
40. Strasser, B., Baudry, J., Mahr, D., Sanchez, G., & Tancoigne, E. (2018). “Citizen Science”? Rethinking Science and Public Participation. Science & Technology Studies, 32, 52-76. https://doi.org/10.23987/sts.60425
https://doi.org/10.23987/sts.60425
 
41. United States Environmental Protection Agency. (1997). Retrieved July 30, 2024, from https://19january2021snapshot.epa.gov/sites/static/files/2015-06/documents/stream.pdf
 
42. Sun, C. C., Hurst, J. E., & Fuller, A. K. (2021). Citizen Science Data Collection for Integrated Wildlife Population Analyses. Frontiers in Ecology and Evolution, 9. https://doi.org/10.3389/fevo.2021.682124
https://doi.org/10.3389/fevo.2021.682124
 
43. Theissinger, K., Fernandes, C., Formenti, G., Bista, I., Berg, P. R., Bleidorn, C., Bombarely, A.,
 
Crottini, A., Gallo, G. R., Godoy, J. A., Jentoft, S., Malukiewicz, J., Mouton, A., Oomen, R. A., Paez, S., Palsbøll, P. J., Pampoulie, C., Ruiz-López, M. J., Secomandi, S., … Zammit, G. (2023). How genomics can help biodiversity conservation. Trends in Genetics, 39(7), 545-559. https://doi.org/10.1016/j.tig.2023.01.005
https://doi.org/10.1016/j.tig.2023.01.005
 
44. Walker, D., Smigaj, M., & Tani, M. (2020). The benefits and negative impacts of citizen science applications to water as experienced by participants and communities. Wiley Interdisciplinary Reviews: Water, 8. https://doi.org/10.1002/wat2.1488
https://doi.org/10.1002/wat2.1488
 
45. Ward, M. (2023). Wildlife Monitoring in Managed Forests: Adapting for Conservation. Journal of Biodiversity Management & Forestry, 2023. https://www.scitechnol.com/peer-review/wildlife- monitoring-in-managed–forests-adapting-for-conservation-EeA1.php?article_id=24603
 
46. WHO. (2022). Biodiversity and ecosystems .:. Sustainable Development Knowledge Platform. https://sustainabledevelopment.un.org/topics/biodiversityandecosystems
 
47. Zwerts, J., Stephenson, P., Maisels, F., Rowcliffe, M., Astaras, C., Jansen, P., van der Waarde, J., Sterck, L., Verweij, P. A., Bruce, T., Brittain, S., & Kuijk, M. (2021). Methods for wildlife monitoring in tropical forests: Comparing human observations, camera traps, and passive acoustic sensors. Conservation Science and Practice, 3. https://doi.org/10.1111/csp2.568
https://doi.org/10.1111/csp2.568