Collaborative extension of biodiversity monitoring protocols in the bird watching community

Citizen science projects have demonstrated the advantages of people with limited relevant prior knowledge participating in research. However, there is a difference between engaging the general public in a scientific project and entering an established expert community to conduct research. This paper describes our ongoing acoustic biodiversity monitoring collaborations with the bird watching community. We report on findings gathered over six years from participation in bird walks, observing conservation efforts, and records of personal activities of experienced birders. We offer an empirical study into extending existing protocols through in-context collaborative design involving scientists and domain experts.

Cows, cockies and atlases : use and abuse of biodiversity monitoring in environmental decision making

Lesson #I. Good long-term monitoring makes for informed and confident decisions on land management.
Lesson #2. Monitoring showing species and habitat decline can directly lead to better protection mechanisms.
Lesson #3. Results of monitoring can be ignored, misused and misquoted to achieve political ends.
Lesson #4. Are we seeing a decline in systematic species surveys by government?
Lesson #5. We don't know enough about what monitoring is happening and why monitoring isn't happening.
Lesson #6. Disparate data sets and cumbersome collection methods are hindering species status monitoring.
Lesson #7. Make better use of existing resomces and expertise.
Lesson #8. Make monitoring data more accessible and enable it to be more repeatable.
Lesson #9. Embed the requirement for monitoring in biodiversity and threatened species legislation.
Lesson #10. Understand better the social elements of ecological monitoring

Improving biodiversity monitoring

Effective biodiversity monitoring is critical to evaluate, learn from, and ultimately improve conservation practice. Well conceived, designed and implemented monitoring of biodiversity should: (i) deliver information on trends in key aspects of biodiversity (e.g. population changes); (ii) provide early warning of problems that might otherwise be difficult or expensive to reverse; (iii) generate quantifiable evidence of conservation successes (e.g. species recovery following management) and conservation failures; (iv) highlight ways to make management more effective; and (v) provide information on return on conservation investment. The importance of effective biodiversity monitoring is widely recognized (e.g. Australian Biodiversity Strategy). Yet, while everyone thinks biodiversity monitoring is a good idea, this has not translated into a culture of sound biodiversity monitoring, or widespread use of monitoring data. We identify four barriers to more effective biodiversity monitoring in Australia. These are: (i) many conservation programmes have poorly articulated or vague objectives against which it is difficult to measure progress contributing to design and implementation problems; (ii) the case for long-term and sustained biodiversity monitoring is often poorly developed and/or articulated; (iii) there is often a lack of appropriate institutional support, co-ordination, and targeted funding for biodiversity monitoring; and (iv) there is often a lack of appropriate standards to guide monitoring activities and make data available from these programmes. To deal with these issues, we suggest that policy makers, resource managers and scientists better and more explicitly articulate the objectives of biodiversity monitoring and better demonstrate the case for greater investments in biodiversitymonitoring. There is an urgent need for improved institutional support for biodiversity monitoring in Australia, for improved monitoring standards, and for improved archiving of, and access to, monitoring data. We suggest that more strategic financial, institutional and intellectual investments in monitoring will lead to more efficient use of the resources available for biodiversity conservation and ultimately better conservation outcomes.

The possibilities of biodiversity monitoring based on Hungarian Light Trap Networks

Our method is presented with displaying time series, consisting of the daily amount of precipitation of 100 years, which has meant a separate challenge, as the precipitation data shows significant deviations. By nowadays, mankind has changed its environment to such an extent that it has a significant effect on other species as well. The Lepidoptera data series of the National Plant Protection and Forestry Light Trap Network can be used to justify this. This network has a national coverage, a large number of collected Lepidoptera, and an available, long data series of several years. For obtaining information from these data, the setting up of an easy to manage database is necessary. Furthermore, it is important to represent our data and our results in an easily analysable and expressive way. In this article the setting up of the database is introduced, together with the presentation of a three dimensional visualization method, which depicts the long-range and seasonal changes together.

Acoustic sensing: Roles and applications in monitoring avian biodiversity

This thesis examined the use of acoustic sensors for monitoring avian biodiversity. Acoustic sensors have the potential to significantly increase the spatial and temporal scale of ecological observations, however acoustic recordings of the environment can be opaque and complex. This thesis developed methods for analysing large volumes of acoustic data to maximise the detection of bird species, and compared the results of acoustic sensor biodiversity surveys with traditional bird survey techniques.

Initial insights on the biodiversity potential of biosolar roofs: A London Olympic Park green roof case study

Cities dominated by impervious artificial surfaces can experience myriad negative environmental impacts. Restoration of green infrastructure has been identified as a mechanism for increasing urban resilience, enabling cities to transition towards sustainable futures in the face of climate-driven change. Building rooftops represent a viable space for integrating new green infrastructure into high density urban areas. Urban rooftops also provide prime locations for photovoltaic (PV) systems. There is increasing recognition that these two technologies can be combined to deliver reciprocal benefits in terms of energy efficiency and biodiversity targets. Scarcity of scientific evaluation of the interaction between PVs and green roofs means that the potential benefits are currently poorly understood. This study documents evidence from a biodiversity monitoring study of a substantial biosolar roof installed in the Queen Elizabeth Olympic Park. Vegetation and invertebrate communities were sampled and habitat structure measured in relation to habitat niches on the roof, including PV panels. Ninety-two plant species were recorded on the roof and variation in vegetation structure associated with proximity to PV panels was identified. Almost 50% of target invertebrate species collected were designated of conservation importance. Arthropod distribution varied in relation to habitat niches on the roof. The overall aim of the MPC green roof design was to create a mosaic of habitats to enhance biodiversity, and the results of the study suggest that PV panels can contribute to niche diversity on a green roof. Further detailed study is required to fully characterise the effects of PV panel density on biodiversity.

Improving assessment and modelling of climate change impacts on global terrestrial biodiversity.

Understanding how species and ecosystems respond to climate change has become a major focus of ecology and conservation biology. Modelling approaches provide important tools for making future projections, but current models of the climate-biosphere interface remain overly simplistic, undermining the credibility of projections. We identify five ways in which substantial advances could be made in the next few years: (i) improving the accessibility and efficiency of biodiversity monitoring data, (ii) quantifying the main determinants of the sensitivity of species to climate change, (iii) incorporating community dynamics into projections of biodiversity responses, (iv) accounting for the influence of evolutionary processes on the response of species to climate change, and (v) improving the biophysical rule sets that define functional groupings of species in global models.

Volunteer-based coral reef monitoring: reliability of data, environmental education and implications for conservation

Coral reefs are the most biodiverse ecosystems of the ocean and they provide notable ecosystem services. Nowadays, they are facing a number of local anthropogenic threats and environmental change is threatening their survivorship on a global scale. Large-scale monitoring is necessary to understand environmental changes and to perform useful conservation measurements. Governmental agencies are often underfunded and are not able of sustain the necessary spatial and temporal large-scale monitoring. To overcome the economic constrains, in some cases scientists can engage volunteers in environmental monitoring. Citizen Science enables the collection and analysis of scientific data at larger spatial and temporal scales than otherwise possible, addressing issues that are otherwise logistically or financially unfeasible. “STE: Scuba Tourism for the Environment” was a volunteer-based Red Sea coral reef biodiversity monitoring program. SCUBA divers and snorkelers were involved in the collection of data for 72 taxa, by completing survey questionnaires after their dives. In my thesis, I evaluated the reliability of the data collected by volunteers, comparing their questionnaires with those completed by professional scientists. Validation trials showed a sufficient level of reliability, indicating that non-specialists performed similarly to conservation volunteer divers on accurate transects. Using the data collected by volunteers, I developed a biodiversity index that revealed spatial trends across surveyed areas. The project results provided important feedbacks to the local authorities on the current health status of Red Sea coral reefs and on the effectiveness of the environmental management. I also analysed the spatial and temporal distribution of each surveyed taxa, identifying abundance trends related with anthropogenic impacts. Finally, I evaluated the effectiveness of the project to increase the environmental education of volunteers and showed that the participation in STEproject significantly increased both the knowledge on coral reef biology and ecology and the awareness of human behavioural impacts on the environment.

Scaling acoustic data analysis through collaboration and automation

Monitoring and assessing environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods of time. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data effectively and efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; collaboration, manual, automatic and human-in-the loop analysis.

Large scale participatory acoustic sensor data analysis : tools and reputation models to enhance effectiveness

Acoustic sensors play an important role in augmenting the traditional biodiversity monitoring activities carried out by ecologists and conservation biologists. With this ability however comes the burden of analysing large volumes of complex acoustic data. Given the complexity of acoustic sensor data, fully automated analysis for a wide range of species is still a significant challenge. This research investigates the use of citizen scientists to analyse large volumes of environmental acoustic data in order to identify bird species. Specifically, it investigates ways in which the efficiency of a user can be improved through the use of species identification tools and the use of reputation models to predict the accuracy of users with unidentified skill levels. Initial experimental results are reported.

Analysing environmental acoustic data through collaboration and automation

Monitoring environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; online collaboration, manual, automatic and human-in-the loop analysis.

Sampling environmental acoustic recordings to determine species richness : I

Acoustic sensors provide an effective means of monitoring biodiversity at large spatial and temporal scales. They can continuously and passively record large volumes of data over extended periods, however these data must be analysed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced users can produce accurate results, however the time and effort required to process even small volumes of data can make manual analysis prohibitive. Our research examined the use of sampling methods to reduce the cost of analysing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilising five days of manually analysed acoustic sensor data from four sites, we examined a range of sampling rates and methods including random, stratified and biologically informed. Our findings indicate that randomly selecting 120, one-minute samples from the three hours immediately following dawn provided the most effective sampling method. This method detected, on average 62% of total species after 120 one-minute samples were analysed, compared to 34% of total species from traditional point counts. Our results demonstrate that targeted sampling methods can provide an effective means for analysing large volumes of acoustic sensor data efficiently and accurately.

Virtual birding : extending an environmental pastime into the virtual world for citizen science

This paper investigates engaging experienced birders, as volunteer citizen scientists, to analyze large recorded audio datasets gathered through environmental acoustic monitoring. Although audio data is straightforward to gather, automated analysis remains a challenging task; the existing expertise, local knowledge and motivation of the birder community can complement computational approaches and provide distinct benefits. We explored both the culture and practice of birders, and paradigms for interacting with recorded audio data. A variety of candidate design elements were tested with birders. This study contributes an understanding of how virtual interactions and practices can be developed to complement existing practices of experienced birders in the physical world. In so doing this study contributes a new approach to engagement in e-science. Whereas most citizen science projects task lay participants with discrete real world or artificial activities, sometimes using extrinsic motivators, this approach builds on existing intrinsically satisfying practices.

Sampling environmental acoustic recordings to determine bird species richness

Acoustic sensors can be used to estimate species richness for vocal species such as birds. They can continuously and passively record large volumes of data over extended periods. These data must subsequently be analyzed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced surveyors can produce accurate results; however the time and effort required to process even small volumes of data can make manual analysis prohibitive. This study examined the use of sampling methods to reduce the cost of analyzing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilizing five days of manually analyzed acoustic sensor data from four sites, we examined a range of sampling frequencies and methods including random, stratified, and biologically informed. We found that randomly selecting 120 one-minute samples from the three hours immediately following dawn over five days of recordings, detected the highest number of species. On average, this method detected 62% of total species from 120 one-minute samples, compared to 34% of total species detected from traditional area search methods. Our results demonstrate that targeted sampling methods can provide an effective means for analyzing large volumes of acoustic sensor data efficiently and accurately. Development of automated and semi-automated techniques is required to assist in analyzing large volumes of acoustic sensor data. Read More: http://www.esajournals.org/doi/abs/10.1890/12-2088.1