RFCX ARBIMON PUBLICATIONS

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Bird Occupancy of a Neotropical Forest is Stable but influenced by Forest age

January 28, 2021

The effects of forest degradation, fragmentation, and climate change occur over long time periods, yet relatively few data are available to evaluate the long-term effects of these disturbances on tropical species occurrence. Here, we quantified changes in occupancy of 50 bird species over 17 years on Barro Colorado Island (BCI), Panama, a model system for the long-term effects of habitat fragmentation.

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Impacts of Drought and Hurricane on tropical bird and frog distributions

January 1, 2021

During the last few decades, much attention has focused on how global change is affecting the environment and species distributions. Land-use change is still the major cause of species declines worldwide, but changes in species distributions have been documented even in pristine and protected areas. Here, we document the distribution dynamics of 26 species of frogs and birds within a Caribbean protected area between 2015 and 2019.

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Acoustic metrics predict habitat type and vegetation structure in the Amazon

June 27, 2020

The rapidly developing field of ecoacoustics offers methods that can advance multi-taxa animal surveys at policy-relevant extents. While the field is promising, there remain foundational assumptions that need to be tested across different biomes before the methods can be applied widely. Here we test two of these assumptions in the Amazon: 1) acoustic indices can be used to predict soundscapes of different habitat types, and 2) acoustic indices are related to vegetation structure.

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Identification of bird and frog species using a convolutional neural network

June 8, 2020

Automated acoustic recorders can collect long-term soundscape data containing species-specific signals in remote environments. Deep learning methods have gained recent attention for automating the process of species identification in soundscape recordings. We present an end-to-end pipeline for training a convolutional neural network (CNN) for multispecies multi-label classification of soundscape recordings, starting from raw, unlabeled audio.

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Multispecies Bioacoustic Classification using transfer learning of deep convolutional
neural networks with pseudo-labeling

April 9, 2020

In this study, we evaluated deep convolutional neural networks for classifying the calls of 24 birds and amphibian species detected in ambient field recordings. In classifying a test set of manually validated positive and negative template based detections, our proposed model achieves 97.7% sensitivity (true positive rate), 96.4% specificity (true negative rate) and 99.5% Area Under a Curve (AUC). This multi-label multi-species classification methodology and its framework can be easily adopted by other acoustic classification problems. Please email us at contact@rfcx.org for a copy of this article.

How does FSC forest certification affect the acoustically active fauna in Peru?

May 22, 2019

Despite several efforts to quantify the effectiveness of forest certification in developing sustainable use of forest resources, there is little evidence that certified forests are more effective in conserving fauna than non-certified managed forest. Our findings correspond with the conclusions of other studies that certified forests can maintain levels of fauna biodiversity similar to those of undisturbed primary forest in the Amazon region.

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Using soundscapes to assess biodiversity in Neotropical oil palm landscapes

April 6, 2019

Expanding oil palm plantations have caused widespread deforestation and biodiversity loss in Southeast Asia, stigmatizing the industry around the world regardless of regional context. In Latin America, oil palm plantations are primarily replacing other agroindustrial land uses with uncertain implications for local biodiversity. Our aim was to create empirical baseline data to help guide development of future plantations into areas where biodiversity impacts are minimized. We used soundscapes to assess fauna in oil palm landscapes of Colombia, the world’s 4th largest palm oil producer.

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It’s time to listen: there is much to be learned from the sounds of tropical ecosystems

June 8, 2018

Soundscape recordings provide a permanent record of a site at a given time and contain a wealth of invaluable and irreplaceable information. In this commentary, we (1) argue for the need to increase acoustic monitoring in tropical systems; (2) describe the types of research questions and conservation issues that can be addressed with passive acoustic monitoring (PAM) using both shortand long-term data in terrestrial and freshwater habitats; and (3) present an initial plan for establishing a global repository of tropical recordings.

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Changes in the acoustic structure and composition along a tropical elevational gradient

December 6, 2017

To improve our understanding of how environmental gradients influence patterns of animal communities and to test the relationship between soundscapes and animal composition we investigated how variation in bird and anuran composition affect the acoustic structure and composition of the soundscapes along an elevation gradient. This study shows how different animal taxa respond to environmental gradients and provide strong evidence for the use of soundscapes as a tool to describe and compare species distribution and composition across large spatial scales.

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Species Richness (of Insects) Drives the Use of Acoustic Space in the Tropics

October 27, 2017

In this manuscript, we evaluate recordings from eight tropical forest sites that vary in species richness, from a relatively low diversity Caribbean forest to a megadiverse Amazonian forest, with the goal of understanding the relationship between acoustic space use (ASU) and species diversity across different taxonomic groups. We show a strong positive relationship between ASU and regional and acoustic morphospecies richness. Premontane forest sites had the highest ASU and the highest species richness, while dry forest and montane sites had lower ASU and lower species richness. Furthermore, we show that insect richness was the best predictor of variation in total ASU, and that insect richness was proportionally greater at high-diversity sites.

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