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.

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

Rapid scanning of spectrograms for efficient identification of bioacoustic events in big data

Acoustic sensing is a promising approach to scaling faunal biodiversity monitoring. Scaling the analysis of audio collected by acoustic sensors is a big data problem. Standard approaches for dealing with big acoustic data include automated recognition and crowd based analysis. Automatic methods are fast at processing but hard to rigorously design, whilst manual methods are accurate but slow at processing. In particular, manual methods of acoustic data analysis are constrained by a 1:1 time relationship between the data and its analysts. This constraint is the inherent need to listen to the audio data. This paper demonstrates how the efficiency of crowd sourced sound analysis can be increased by an order of magnitude through the visual inspection of audio visualized as spectrograms. Experimental data suggests that an analysis speedup of 12× is obtainable for suitable types of acoustic analysis, given that only spectrograms are shown.

The use of acoustic indices to determine avian species richness in audio-recordings of the environment

Interpreting acoustic recordings of the natural environment is an increasingly important technique for ecologists wishing to monitor terrestrial ecosystems. Technological advances make it possible to accumulate many more recordings than can be listened to or interpreted, thereby necessitating automated assistance to identify elements in the soundscape. In this paper we examine the problem of estimating avian species richness by sampling from very long acoustic recordings. We work with data recorded under natural conditions and with all the attendant problems of undefined and unconstrained acoustic content (such as wind, rain, traffic, etc.) which can mask content of interest (in our case, bird calls). We describe 14 acoustic indices calculated at one minute resolution for the duration of a 24 hour recording. An acoustic index is a statistic that summarizes some aspect of the structure and distribution of acoustic energy and information in a recording. Some of the indices we calculate are standard (e.g. signal-to-noise ratio), some have been reported useful for the detection of bioacoustic activity (e.g. temporal and spectral entropies) and some are directed to avian sources (spectral persistence of whistles). We rank the one minute segments of a 24 hour recording in descending order according to an "acoustic richness" score which is derived from a single index or a weighted combination of two or more. We describe combinations of indices which lead to more efficient estimates of species richness than random sampling from the same recording, where efficiency is defined as total species identified for given listening effort. Using random sampling, we achieve a 53% increase in species recognized over traditional field surveys and an increase of 87% using combinations of indices to direct the sampling. We also demonstrate how combinations of the same indices can be used to detect long duration acoustic events (such as heavy rain and cicada chorus) and to construct long duration (24 h) spectrograms.