Safari Science: assessing the reliability of citizen science data for wildlife surveys

Opportunities and limitations: A comparative analysis of citizen science and expert recordings for bioacoustic research

Citizen science is an approach that has become increasingly popular in recent years. Despite this growing popularity, there still is widespread scepticism in the academic world about the validity and quality of data from citizen science projects. And although there might be great potential, citizen science is a rarely used approach in the field of bioacoustics. To better understand the possibilities, but also the limitations, we here evaluated data generated in a citizen science project on nightingale song as a case study. We analysed the quantity and quality of song recordings made in a non-standardized way with a smartphone app by citizen scientists and the standardized recordings made with professional equipment by academic researchers. We made comparisons between the recordings of the two approaches and among the user types of the app to gain insights into the temporal recording patterns, the quantity and quality of the data. To compare the deviation of the acoustic parameters in the recordings with smartphones and professional devices from the original song recordings, we conducted a playback test. Our results showed that depending on the user group, citizen scientists produced many to a lot of recordings of valid quality for further bioacoustic research. Differences between the recordings provided by the citizen and the expert group were mainly caused by the technical quality of the devices used—and to a lesser extent by the citizen scientists themselves. Especially when differences in spectral parameters are to be investigated, our results demonstrate that the use of the same high-quality recording devices and calibrated external microphones would most likely improve data quality. We conclude that many bioacoustic research questions may be carried out with the recordings of citizen scientists. We want to encourage academic researchers to get more involved in participatory projects to harness the potential of citizen science—and to share scientific curiosity and discoveries more directly with society.

The impact of RHDV-K5 on rabbit populations in Australia: an evaluation of citizen science surveys to monitor rabbit abundance

The increasing popularity of citizen science in ecological research has created opportunities for data collection from large teams of observers that are widely dispersed. We established a citizen science program to complement the release of a new variant of the rabbit biological control agent, rabbit haemorrhagic disease virus (RHDV), known colloquially as K5, across Australia. We evaluated the impact of K5 on the national rabbit population and compared citizen science and professionally-collected spotlight count data. Of the citizen science sites (n = 219), 93% indicated a decrease in rabbit abundance following the release of K5. The overall finite monthly growth rate in rabbit abundance was estimated as 0.66 (95%CI, 0.26, 1.03), averaging a monthly reduction of 34% at the citizen science sites one month after the release. No such declines were observed at the professionally monitored sites (n = 22). The citizen science data submissions may have been unconsciously biased or the number of professional sites may have been insufficient to detect a change. Citizen science participation also declined by 56% over the post-release period. Future programs should ensure the use of blinded trials to check for unconscious bias and consider how incentives and/or the good will of the participants can be maintained throughout the program.

Tourist photographs as a scalable framework for wildlife monitoring in protected areas

Protected areas are critical to conservation efforts in the face of rapid biodiversity declines [1]. Yet the resources for conservation are often limited and shared amongst many competing priorities [2]. As a consequence, even basic monitoring surveys are absent within most protected areas [3]. Although a range of wildlife monitoring methods exist, considerable focused survey effort is often required to yield accurate and precise estimates [4]. This makes monitoring difficult to sustain or replicate, limiting access to the data required for evidence-based conservation decisions. Citizen-scientists have been proposed as an important complement to the finite resources available for basic monitoring within protected areas [5]; however, the full potential of this approach has yet to be realised. Wildlife tourists and guides are especially focussed on encountering and photographing fauna and flora, yet the data collected in these efforts is rarely harnessed for conservation monitoring within protected areas. A detailed understanding of photographic tourism's potential role in wildlife monitoring has been lacking, but is essential for the development of new tools to harness the data being collected through tourism. Here, we demonstrate that tourist-contributed data can aid wildlife monitoring in protected areas by providing population estimates of large carnivores comparable to those from traditional survey methods. Our approach could capitalize upon the immense number of wildlife photographs being taken daily as part of the global > 30-billion USD, wildlife-based tourism industry.

The potential for citizen science to produce reliable and useful information in ecology

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass-participation internet-based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high-quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state-of-the-art analytical methods, and well-supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.

Opportunistic citizen science data transform understanding of species distributions, phenology, and diversity gradients for global change research

Opportunistic citizen science (CS) programs allow volunteers to report species observations from anywhere, at any time, and can assemble large volumes of historic and current data at faster rates than more coordinated programs with standardized data collection. This can quickly provide large amounts of species distributional data, but whether this focus on participation comes at a cost in data quality is not clear. Although automated and expert vetting can increase data reliability, there is no guarantee that opportunistic data will do anything more than confirm information from professional surveys. Here, we use eButterfly, an opportunistic CS program, and a comparable dataset of professionally collected observations, to measure the amount of new distributional species information that opportunistic CS generates. We also test how well opportunistic CS can estimate regional species richness for a large group of taxa (>300 butterfly species) across a broad area. We find that eButterfly contributes new distributional information for >80% of species, and that opportunistically submitting observations allowed volunteers to spot species ~35 days earlier than professionals. Although eButterfly did a relatively poor job at predicting regional species richness by itself (detecting only about 35-57% of species per region), it significantly contributed to regional species richness when used with the professional dataset (adding ~3 species that had gone undetected in professional surveys per region). Overall, we find that the opportunistic CS model can provide substantial complementary species information when used alongside professional survey data. Our results suggest that data from opportunistic CS programs in conjunction with professional datasets can strongly increase the capacity of researchers to estimate species richness, and provide unique information on species distributions and phenologies that are relevant to the detection of the biological consequences of global change.