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Impact of Human Practices on Environmental Degradation

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“Community monitoring and citizen science monitors are more effective than experimental ecology at detecting environmental degradation”

Currently, a number of detrimental human-induced factors such as mining, desertification, deforestation, air and water pollution are accelerating environmental degradation which is cause for global concern (Freeman, 2015). Human effects on environmental degradation are so intense that they have determined a new geologic era which is named the Anthropocene (Crutzen, 2002). In short, environmental degradation involves the gradual degrading of the environment and can be broken into three types; land, air and water degradation. Firstly, the degradation of the atmosphere is due to a rise in greenhouse gases, carbon dioxide (CO2) and methane (CH4), as a result of human-induced factors such as electricity and heat generation, agriculture and transportation. The results of the increase in greenhouse gases lead to human health implications, with pollution resulting in premature deaths (Palagiano & Akhtar, 2017). Secondly, water quality impacts result from human practices, for example agriculture where 70% of freshwater withdrawal is used for irrigation (Zolnikov, 2018). Overall, the acceleration of climate change from human induced practices results in increased precipitation, chemical runoff, therefore contaminating water sources, reduced snow cover and many other impacts (Zolnikov, 2018). Thirdly, land degradation is mainly due to agriculture and land use such as mining and farming with impacts including soil erosion, loss of species biodiversity and a reduction in food production (Stefanski & Sivakumar, 2007) . Land degradation is a global concern in terms of food production as only 11% of the global land surface is considered suitable for food production and production needs to account for the estimated 8.2 billion world population in 2020 (Stefanski & Sivakumar, 2007).

Taking into account all of the factors impacting the environment it is imperative that environmental degradation is monitored as the consequences are all-encompassing. Ways to monitor environmental degradation include various methods two of which include community monitoring and citizen science monitors and experimental ecology.

Community monitoring and citizen science monitors are one method to monitor and detect environmental degradation. Community monitoring and citizen science has evolved and developed greatly to aid research into both small- and large-scale projects related to ecology and climate change. More specifically, citizen science involves the participation and collaboration of the public in scientific research through methods such as crowdsourcing, community-based monitoring and action research in order to thoroughly collate and analyse data related to a localised hypothesis driven research methods (Dickinson, Zuckerberg & Bonter, 2010; Newman, et al., 2017). A wide range of people from different backgrounds and levels of experience are involved, including government agencies, academia and community groups, to collaborate in monitoring local or wider scientific issues (Newman, et al., 2017).

Citizen science is not new and has grown over the past three decades with projects such as astronomy and ornithology, for example the Transit of Venus project which was designed and funded by the British government in 1874 to determine the Earth’s distance from the sun (Dickinson, Zuckerberg, & Bonter, 2010). Its growth more recently has been due to advances in technology and equipment such as tracking apps, cameras, phones and the internet which allow volunteers to collect and input data more efficiently and increases volunteers ability to track local and large-scale environmental changes and degradation (Dickinson, Zuckerberg & Bonter, 2010; Silvertown, 2009). Along with technological advancements, citizen science has developed a stronger relationship and awareness of issues between both the public and the scientific community (Newman, et al., 2017; Bonney, et al., 2009). Citizen sciences’ relationship with ecology has been long lasting as citizen science yields important contributions to ecology through hundreds of millions of observational data on weather, bird migrations, flowering patterns, and insect flight times (Miller-Rushing, Gallinat, & Primack, 2019). Citizen science allows ecologists to design their own projects to cater for a broader topic area or a more specific hypothesis whilst maintaining an achievable budget. This freedom enables ecologists to use the tools of citizen science to explore environmental changes and degradation of the environment and the phenology, abundance, survival and reproductive success of organisms that reside within the environment (Dickinson, Zuckerberg, & Bonter, 2010).

In comparison, experimental ecology, consists of the use of experimental methods such as sampling, models and replications through the use of either a mensurative or manipulative experiment, to aid in understanding the relationships between living organisms and their surrounding environments (Clapham, 1966). Observation are made during these methods whereby a hypothesis, or plausible explanation for these observations is devised, for example hypothesising an invasive species effect on vegetation (Hairston SR, 1989). As mentioned above, experimental ecology can take place through field studies, laboratory studies or a mixture of the two. Findings from these experiments are collated and transformed to provide conclusions on small-and-large scale projects with themes such as species abundance and trophic interactions, plant growth time scale and animal migratory patterns. The use of experimental ecology is important to help provide solutions to current environmental issues, such as environmental degradation, in the fast-changing world of today (Clapham, 1966).

Comparing the two methods it is evident that both provide successful and unsuccessful results in detecting environmental degradation.

One example of a successful citizen science project is Hassall et al, designed a model to identify hymenopteran compared to syrphid mimic (Miller-Rushing, Gallinat, & Primack, 2019). Instead of directly observing model-mimic relationships in the field, Hassall et al, designed an online interactive module where citizen scientist could determine visual similarities of pairs of syrphids and hymenopterans side by side through a rating system (Miller-Rushing, Gallinat, & Primack, 2019). The similarities were based off factors such as size, colour, shape, hairiness and more (Miller-Rushing, Gallinat, & Primack, 2019). Through the outlet of twitter, they received 30,300 ratings where 237 high-fidelity model-mimic pairs were identified out of 2,325 potential combinations (Miller-Rushing, Gallinat, & Primack, 2019). The advantages of this specific experiment included, volunteers could easily identify pairwise combinations faster, and therefore greater ratings could be achieved. The module avoided possible labour-intensive field observations as well as difficult to get to locations which in comparison to experimental ecology is sometimes unavoidable (Miller-Rushing, Gallinat, & Primack, 2019).

It is evident, however, that unlike experiment ecology there are more issues surrounding the perception that the data collected by community monitoring and citizen science monitors contain higher levels of both variability and bias, compared to data collected by ecologists in experimental ecology ( Bird, et al., 2014). These concerns stem mostly from a lack of studies comparing the quality of volunteers versus professionally collected data, with more papers written on the unreliable data that volunteers collect (Fitzpatrick, Preisser, Ellison, & Elkinton, 2009). Other concerns include projects are solely volunteer based; therefore, the data is coming from people who elect to take part in the project and not a random sample of people. For example, sources of bias may be present in data, such as under-detection and misidentification of species (Crall, Newman, Stohlgren, Holfelder, Graham, & Waller, 2011; Sheppard, Wiggins, & Terveen, 2014). When volunteers are identifying invasive species, there is a high probability that the individual with falsely observe a species therefore adding to the possibility of undocumented sampling bias within the final data set (Fitzpatrick, Preisser, Ellison, & Elkinton, 2009). The highest probability is observing a species as present when it is not actually present, this is a type 1 error (Fitzpatrick, Preisser, Ellison, & Elkinton, 2009). To combat this example of possible sampling bias and validity problems a site occupying model was developed by MacKenzie et al, 2006, to estimate the occupation of species given that a species are more likely to be detected imperfectly, initially the model included only” false negatives”, but further improvement included “false positives” (Fitzpatrick, Preisser, Ellison, & Elkinton, 2009).

One of the main differences between community monitoring and citizen science monitors and experimental ecology is the level of knowledge and training. This perception might be due to the fact that community monitoring and citizen science is volunteered based and includes differing skills, dedication and training of the volunteers involved ( Bird, et al., 2014). Whereas experimental ecology projects are conducted by professional scientists in ecology. However, it is evident from reviews of community monitoring and citizen science that volunteers given the correct training and guidance will be able to collect sufficient data for their specific project (Cohn, 2008). When designing community monitoring and citizen science monitor projects it is important that scientists set out guidelines and protocols, such as guidebooks and other material, about how to use certain equipment or where to input data (Cohn, 2008). This would be helpful for volunteers and possibly reduce chances of bias or variability in their data (Cohn, 2008). Although it is more likely that professional ecologists would perform tasks more successfully, it is increasingly becoming apparent that volunteers in citizen science projects are themselves scientists and teachers (Cohn, 2008). A previous study on the comparison between observers and experienced observers in the detection of low-density infestations on trees found that although experienced observers identified the most infestations, they misclassified more trees than the volunteers did (Fitzpatrick, Preisser, Ellison, & Elkinton, 2009). This is interesting to note as sometimes community monitors and citizen science monitors are able to detect environmental degradation with a new set of eyes, something that may not occur in experimental ecology.

In conclusion, it is evident from the examples presented above, that community monitoring and citizen science monitors are effective in detecting areas of environmental degradation in comparison to experimental ecology. Community monitoring and citizen science monitors have proven to be more time efficient and cost efficient for a number of reasons (Follett & Strezov, 2015). Ecologists have the freedom to design their own projects, including certain protocols and instructions for volunteers, and are able to distribute their projects through mediums such as social networks, online websites and community-based work. These advantages provide ecologists with large data sets and observations that decrease the strenuous workload that would accompany field experiments and would otherwise be too time consuming and difficult to gather. As well as advantages for ecologists, citizen science also increasing community awareness by increasing scientific knowledge and communication between the scientific community and the public, also allowing for an enhancement in research design and improved data processing (Schroter, et al., 2017). There is a long running debate about what methods are more successful and beneficial in experimental ecology, community and volunteer-based work might is the answer to better detect environmental degradation, especially with the advancements in technology and convenience in the world today (Werner, 1998).

References

  1. Bird, T. J., Bates, A. E., Lefcheck, J. S., Hill, N. A., Thomson, R. J., Edgar, G. J., . . . Frusher, S. (2014, May ). Statistical solutions for error and bias in global citizen science datasets. Biological Conservation, 173, 144-154.
  2. Clapham, A. R. (1966). What Is Experimental Ecology? Folia Geobotanica & Phytotaxonomica, 88-92.
  3. Cohn, J. P. (2008). Citizen Science: Can Volunteers Do Real Research? BioScience, 192-197.
  4. Crall, A. W., Newman, G., Stohlgren, T. J., Holfelder, K. A., Graham , J., & Waller, D. M. (2011). Assessing citizen science data quality: a case study. 433-442.
  5. Crutzen, P. J. (2002). The “Anthropocene”. In Journal de Physique IV, 1-5.
  6. Dickinson, J. L., Zuckerberg , B., & Bonter, D. N. (2010). Citizen Science as an Ecological Research Tool: Challenges and Benefits. Annual Review of Ecology, Evolution, and Systematics, 41, 149-172.
  7. Dickinson, J. L., Zuckerberg, B., & Bonter, D. N. (2010). Citizen Science as an Ecological Research Tool: Challenges and Benefits. Annual Review of Ecology, Evolution, and Systematics, 149-172.
  8. Fitzpatrick, M. C., Preisser, E. L., Ellison, A. M., & Elkinton, J. S. (2009). Observer bias and the detection of low‐density populations. Ecological Applications , 1673-1679.
  9. Follett, R., & Strezov, V. (2015). An Analysis of Citizen Science Based Research: Usage and Publication Patterns. PLOS One , 10.
  10. Freeman, S. (2015). Environmental Conservation and Development: Critical Perspectives. International Encyclopedia of the Social & Behavioral Sciences (Second Edition), 713-719.
  11. Hairston SR, N. G. (1989). Ecological problems and how they are approached . In N. G. Hairston SR, Ecological Experiments. Cambridge : Press Syndicate of the University of Cambridge .
  12. Miller-Rushing, A. J., Gallinat, A. S., & Primack, R. B. (2019). Creative citizen science illuminates complex ecological responses to climate change. PNAS, 720-722.
  13. Newman, G., Chandler, M., Clyde, M., McGreavy, B., Haklay, M., Ballard, H., . . . Gallo, J. (2017). Leveraging the power of place in citizen science for effective conservation decision making. Biological Conservation , 55-64.
  14. Palagiano, C., & Akhtar, R. (2017). Climate Change and Air Pollution: An Introduction. Climate Change and Air Pollution, 3-8.
  15. Schroter, M., Kraemer, R., Mantel, M., Kabisch, N., Hecker, S., Richter, A., . . . Bonn, A. (2017). Citizen science for assessing ecosystem services: Status, challenges and opportunities. Ecosystem Services, 80-94.
  16. Sheppard, S. A., Wiggins, A., & Terveen , L. (2014). Capturing quality: retaining provenance for curated volunteer monitoring data. Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing Pages, 1234-1245.
  17. Silvertown, J. (2009). A new dawn for citizen science. Trends in Ecology & Evolution , 467-471.
  18. Stefanski, R., & Sivakumar, M. V. (2007). Climate and Land Degradation — an Overview. Climate and Land Degradation, 105-135.
  19. Trushkowsky, B., Kraska, T., Franklin, M. J., & Sarkar, P. (2013). Crowdsourced enumeration queries. Data Engineering (ICDE), 2013 IEEE 29th International Conference on, 673 – 684.
  20. Werner, E. E. (1998). Ecological Experiments and a Research Program in Community Ecology. In E. E. Werner, Experimental Ecology; Issues and Perspectives (pp. 183-202). New York : Oxford University Press.
  21. Zolnikov, T. R. (2018). Climate Change: Water and Sanitation. Climate Resilient Water Resources Management , 5-14.

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