Building capacity in biodiversity monitoring at the global scale

Human-driven global change is causing ongoing declines in biodiversity worldwide. In order to address these declines, decision-makers need accurate assessments of the status of and pressures on biodiversity. However, these are heavily constrained by incomplete and uneven spatial, temporal and taxonomic coverage. For instance, data from regions such as Europe and North America are currently used overwhelmingly for large-scale biodiversity assessments due to lesser availability of suitable data from other, more biodiversity-rich, regions. These data-poor regions are often those experiencing the strongest threats to biodiversity, however. There is therefore an urgent need to fill the existing gaps in global biodiversity monitoring. Here, we review current knowledge on best practice in capacity building for biodiversity monitoring and provide an overview of existing means to improve biodiversity data collection considering the different types of biodiversity monitoring data. Our review comprises insights from work in Africa, South America, Polar Regions and Europe; in government-funded, volunteer and citizen-based monitoring in terrestrial, freshwater and marine ecosystems. The key steps to effectively building capacity in biodiversity monitoring are: identifying monitoring questions and aims; identifying the key components, functions, and processes to monitor; identifying the most suitable monitoring methods for these elements, carrying out monitoring activities; managing the resultant data; and interpreting monitoring data. Additionally, biodiversity monitoring should use multiple approaches including extensive and intensive monitoring through volunteers and professional scientists but also harnessing new technologies. Finally, we call on the scientific community to share biodiversity monitoring data, knowledge and tools to ensure the accessibility, interoperability, and reporting of biodiversity data at a global scale

Methodology for ecosystem change assessing using ecoacoustics analysis

RESUMEN: La ecoacústica se ha convertido en un área de creciente interés para el monitoreo de ecosistemas. Entre las principales ventajas que presenta sobre las técnicas tradicionales se encuentran su bajo costo, poca afectación al entorno y simplicidad; además de que la distribución de varias grabadoras hace posible la recolección de más información. Sin embargo, para estudios de largo plazo, la cantidad de datos hace que la inspección manual de las grabaciones sea una tarea tediosa y por consiguiente el análisis sea limitado. Como alternativa a la inspección manual, una serie de índices han sido propuestos para resumir la información acústica de las grabaciones. No obstante, estos índices han sido aplicados principalmente a estudios de biodiversidad y su relación con el estado del ecosistema no es claro aún. En este trabajo se confió en la robustez del ANOVA frente a datos que no se distribuyen normalmente para proponer una metodología de selección de los mejores índices o descriptores acústicos para una aplicación específica y usarlos para modelar los patrones del paisaje acústico del ecosistema con modelos ocultos de Markov y emisiones por mezclas Gaussianas (GMMHMM). Además, el conjunto de descriptores que entran al modelo incluye por defecto un indicador de biodiversidad para cada banda de 1kHz. Esta metodología fue aplicada a dos casos colombianos con tipos de ecosistema definidos. En el primer caso, una serie de grabaciones de bosque, rastrojo y pastizal fueron colectados por más de un año en el este de Antioquia. La segunda aplicación buscaba encontrar patrones de paisaje acústico de las transformaciones de bosque seco en dos regiones del caribe colombiano. El modelo identificó seis y tres patrones acústicos para la primera y segunda base de datos respectivamente. En la primera aplicación, se encontraron sonidos continuos, alta intensidad biofónica y ocupación de varias bandas en los patrones asociados a bosque, mientras que en los rastrojos se presentó más entropía, que se relaciona con alta presencia geofónica, lo que limita la actividad biofónica. Finalmente los paisajes acústicos de pastizal alternaron entre periodos de alta geofonía y alta complejidad frecuencial, haciéndolo un ecosistema intermedio en el sentido acústico. La adaptación del modelo para clasificación resultó en la identificación del 81% de las muestras de bosque, 96,6 % de las muestras de rastrojo y 51,2 % de las muestras de pastizal. Los resultados de clasificación para la segunda aplicación no fueron altos, con 68% para las muestras de baja transformación, 58,9% para la transformación media y 31,8% para la transformación alta. No obstante, las matrices de confusión indicaron que las muestras de entrenamiento no fueron suficientes, y que debería proporcionarse mayor muestreo para obtener mejores resultados. Dado que GMMHMM es un modelo secuencial, también presentó la configuración temporal de los patrones acústicos dadas sus probabilidades de transición. Esta característica nos permitió destacar la importancia de la conservación, cuando encontramos que los estados más estables e inaccesibles fueron asociados a los ecosistemas más diversos acústicamente.ABSTRACT: Ecoacoustics has become a field of growing interest for ecosystem monitoring. Its main advantages over traditional methods include cost effectiveness, non-invasiveness and simplicity; besides the distribution of many recorder units makes possible the recollection of more information. However, for long term studies, the quantity of collected data makes the manual inspection of recordings a cumbersome task, leading to reduced analysis. As an alternative to manual inspection, a series of indices have been proposed to summarize the acoustical information in recordings. Nonetheless, these indices have been applied mainly to biodiversity studies and their connection to ecosystem state is still not clear. In this work we trusted ANOVA robustness for non-normal data for proposing a methodology that selected the best acoustical indices or features for a specific application and used them to model the ecosystem soundscape patterns with hidden Markov models and Gaussian mixture emissions (GMMHMM). Additionally, the set of input features included by default a biodiversity indicator per 1kHz band. This methodology was applied to two Colombian cases with defined ecosystem types. In the first case, a series of forest, stubble and pasture recordings were collected for over a year in the east of Antioquia. The second application aimed to find the soundscape patterns of dry forests transformations in two regions of the Colombian Caribbean. The model identified six and three soundscape patterns for the first and second dataset respectively. In the first application, continuous sounds, high biophonic intensity and multiple occupied frequency bands were found in the patterns associated to forest sites; on the other hand, stubble sites presented more general entropy, which we related to high geophonic presence, preventing biophonic activity. Lastly, pasture soundscapes alternated between periods of high geophony and high frequency complexity, making it an intermediate ecosystem in the acoustical sense. The adaptation of the model for classification resulted in the identification of 81% of the forest samples, 96.6% of the stubble samples and 51.2% of the pasture samples. The classification results for the second application were not as high, with 68% for the low transformation samples, 58.8% for the medium transformation and 31.8% for the high transformation. Nonetheless, the confusion matrices indicated that the training samples were not enough, and more sampling should be provided for attaining better results. Given that GMMHMM is a sequential model, it also presented the temporal configuration of the acoustical patterns by their transition probabilities. This feature allowed us to emphasize the importance of conservation, when we found that the most stable and inaccessible states were associated to the most acoustically diverse ecosystems

Living Earth Community

"Living Earth Community: Multiple Ways of Being and Knowing is a celebration of the diversity of ways in which humans can relate to the world around them, and an invitation to its readers to partake in planetary coexistence. Innovative, informative, and highly accessible, this interdisciplinary anthology of essays brings together scholars, writers and educators across the sciences and humanities, in a collaborative effort to illuminate the different ways of being in the world and the different kinds of knowledge they entail – from the ecological knowledge of Indigenous communities, to the scientific knowledge of a biologist and the embodied knowledge communicated through storytelling. This anthology examines the interplay between Nature and Culture in the setting of our current age of ecological crisis, stressing the importance of addressing these ecological crises occurring around the planet through multiple perspectives. These perspectives are exemplified through diverse case studies – from the political and ethical implications of thinking with forests, to the capacity of storytelling to motivate action, to the worldview of the Indigenous Okanagan community in British Columbia. Living Earth Community: Multiple Ways of Being and Knowing synthesizes insights from across a range of academic fields, and highlights the potential for synergy between disciplinary approaches and inquiries. This anthology is essential reading not only for researchers and students, but for anyone interested in the ways in which humans interact with the community of life on Earth, especially during this current period of environmental emergency.