Acoustic monitoring of Amazonian wildlife in human-modified landscapes

Tropical forest covers just 12% of the planet’s land surface, but disproportionately host the planet’s biodiversity, including around two thirds of all terrestrial species. Amazonia retains the largest extent of remaining tropical forest globally, but just over 50% of all tropical forest loss since 2002 has been in the region. Deforestation and disturbance result in significant loss in forest biodiversity, but quantifying the exact nature of those changes can be complex. The Amazon represents a particularly challenging case in which to assess biodiversity change due to the spatiotemporal scales being assessed, because of the high proportion of rare species, and the challenging conditions for conducting biodiversity surveys in tropical forest. Ecoacoustics has been championed as a valuable tool to overcome the difficulties of monitoring in such conditions and at large spatio-temporal scales, but applied analytical methods often remain underdeveloped. In this this thesis I develop and use a range of ecoacoustic methods to help understand the impact of anthropogenic disturbance on Amazonian wildlife, using an extensive audio dataset collected from survey points spanning a degradation gradient in the Eastern Brazilian Amazon. In Chapter 2 I introduce a quick and simple method for the detection of rainfall, tested for efficacy globally and with an accompanying R package. In Chapter 3 I present a new approach to subsampling of acoustic data for manual assessment of avian biodiversity, finding that using a high number of short repeat samples can detect approximately 50% higher alpha diversity than more commonly used approaches. In Chapter 4 I assess the sensitivity and fidelity of two commonly used acoustic indices to biodiversity responses to forest disturbances, finding that measuring indices at narrower, ecologically appropriate time-frequency bins avoids problems with signal masking. In Chapter 5 I use a two-stage, random forest based method to build a multi-taxa classifier for the nocturnal avifaunal community in the study region, and use the classifier-derived data to reveal that the nocturnal bird community is largely robust to less intense forms of forest disturbance. Overall, in this thesis I demonstrate that ecoacoustics can be a highly effective method for inventorying and monitoring biodiversity in one of the most diverse and challenging regions on the planet

Detecting and reducing heterogeneity of error in acoustic classification

Passive acoustic monitoring can be an effective method for monitoring species, allowing the assembly of large audio datasets, removing logistical constraints in data collection and reducing anthropogenic monitoring disturbances. However, the analysis of large acoustic datasets is challenging and fully automated machine learning processes are rarely developed or implemented in ecological field studies. One of the greatest uncertainties hindering the development of these methods is spatial generalisability—can an algorithm trained on data from one place be used elsewhere? We demonstrate that heterogeneity of error across space is a problem that could go undetected using common classification accuracy metrics. Second, we develop a method to assess the extent of heterogeneity of error in a random forest classification model for six Amazonian bird species. Finally, we propose two complementary ways to reduce heterogeneity of error, by (i) accounting for it in the thresholding process and (ii) using a secondary classifier that uses contextual data. We found that using a thresholding approach that accounted for heterogeneity of precision error reduced the coefficient of variation of the precision score from a mean of 0.61 ± 0.17 (SD) to 0.41 ± 0.25 in comparison to the initial classification with threshold selection based on F-score. The use of a secondary, contextual classification with thresholding selection accounting for heterogeneity of precision reduced it further still, to 0.16 ± 0.13, and was significantly lower than the initial classification in all but one species. Mean average precision scores increased, from 0.66 ± 0.4 for the initial classification, to 0.95 ± 0.19, a significant improvement for all species. We recommend assessing—and if necessary correcting for—heterogeneity of precision error when using automated classification on acoustic data to quantify species presence as a function of an environmental, spatial or temporal predictor variable

Acoustic indices perform better when applied at ecologically meaningful time and frequency scales

Acoustic indices are increasingly employed in the analysis of soundscapes to ascertain biodiversity value. However, conflicting results and lack of consensus on best practices for their usage has hindered their application in conservation and land‐use management contexts. Here we propose that the sensitivity of acoustic indices to ecological change and fidelity of acoustic indices to ecological communities are negatively impacted by signal masking. Signal masking can occur when acoustic responses of taxa sensitive to the effect of interest are masked by less sensitive acoustic groups, or target taxa sonification is masked by non‐target noise. We argue that by calculating acoustic indices at ecologically appropriate time and frequency bins, masking effects can be reduced and the efficacy of indices increased. We test this on a large acoustic dataset collected in Eastern Amazonia spanning a disturbance gradient of undisturbed, logged, burned, logged‐and‐burned, and secondary forests. We calculated values for two acoustic indices: the Acoustic Complexity Index and the Bioacoustic Index, across the entire frequency spectrum (0‐22.1 kHz), and four narrower subsets of the frequency spectrum; at dawn, day, dusk and night. We show that signal masking has a large impact on the sensitivity of acoustic indices to forest disturbance classes. Calculating acoustic indices at a range of narrower time‐frequency bins substantially increases the classification accuracy of forest classes by random forest models. Furthermore, signal masking led to misleading correlations, including spurious inverse correlations, between biodiversity indicator metrics and acoustic index values compared to correlations derived from manual sampling of the audio data. Consequently, we recommend that acoustic indices are calculated either at a range of time and frequency bins, or at a single narrow bin, predetermined by a priori ecological understanding of the soundscape

Optimizing tropical forest bird surveys using passive acoustic monitoring and high temporal resolution sampling

Estimation of avian biodiversity is a cornerstone measure of ecosystem condition. Surveys conducted using autonomous recorders are often more efficient at estimating diversity than traditional point-count surveys. However, there is limited research into the optimal temporal resolution for sampling—the trade-off between the number of samples and sample duration when sampling a survey window with a fixed survey effort—despite autonomous recorders allowing easy repeat sampling compared to traditional survey methods. We assess whether the additional temporal coverage from high temporal resolution (HTR) sampling, consisting of 240 15-s samples spread randomly across a survey window detects higher alpha and gamma diversity than low temporal resolution (LTR) sampling of four 15-min samples at the same locations. We do so using an acoustic dataset collected from 29 locations in a region of very high avian biodiversity—the eastern Brazilian Amazon. We find HTR sampling outperforms LTR sampling in every metric considered, with HTR sampling predicted to detect approximately 50% higher alpha diversity, and 10% higher gamma diversity. This effect is primarily driven by increased coverage of variation in detectability across the morning, with the earliest period containing a distinct community that is often under sampled using LTR sampling. LTR sampling produced almost four times as many false absences for species presence. Additionally, LTR sampling incorrectly found 70 species (34%) at only a single forest type when they were in fact present in multiple forest types, while the use of HTR sampling reduced this to just two species (0.9%). When considering multiple independent detections of species, HTR sampling detected three times more uncommon species than LTR sampling. We conclude that high temporal resolution sampling of passive-acoustic monitoring-based surveys should be considered the primary method for estimating the species richness of bird communities in tropical forests

Listening to tropical forest soils

Acoustic monitoring has proven to be an effective tool for monitoring biotic soundscapes in the marine, terrestrial, and aquatic realms. Recently it has been suggested that it could also be an effective method for monitoring soil soundscapes, but has been used in very few studies, primarily in temperate and polar regions. We present the first study of soil soundscapes using passive acoustic monitoring in tropical forests, using a novel analytical pipeline allowing for the use of in-situ recording of soundscapes with minimal soil disturbance. We found significant differences in acoustic index values between burnt and unburnt forests and the first indications of a diel cycle in soil soundscapes. These promising results and methodological advances highlight the potential of passive acoustic monitoring for large-scale and long-term monitoring of soil biodiversity. We use the results to discuss research priorities, including relating soil biophony to community structure and ecosystem function, and the use of appropriate hardware and analytical techniques

Granulovirus PK-1 kinase activity relies on a side-to-side dimerization mode centered on the regulatory αC helix

The life cycle of Baculoviridae family insect viruses depends on the viral protein kinase, PK-1, to phosphorylate the regulatory protein, p6.9, to induce baculoviral genome release. Here, we report the crystal structure of Cydia pomenella granulovirus PK-1, which, owing to its likely ancestral origin among host cell AGC kinases, exhibits a eukaryotic protein kinase fold. PK-1 occurs as a rigid dimer, where an antiparallel arrangement of the αC helices at the dimer core stabilizes PK-1 in a closed, active conformation. Dimerization is facilitated by C-lobe:C-lobe and N-lobe:N-lobe interactions between protomers, including the domain-swapping of an N-terminal helix that crowns a contiguous β-sheet formed by the two N-lobes. PK-1 retains a dimeric conformation in solution, which is crucial for catalytic activity. Our studies raise the prospect that parallel, side-to-side dimeric arrangements that lock kinase domains in a catalytically-active conformation could function more broadly as a regulatory mechanism among eukaryotic protein kinases

hardRain: An R package for quick, automated rainfall detection in ecoacoustic datasets using a threshold-based approach

The increasing demand for cost-efficient biodiversity data at large spatiotemporal scales has led to an increase in the collection of large ecoacoustic datasets. Whilst the ease of collection and storage of audio data has rapidly increased and costs fallen, methods for robust analysis of the data have not developed so quickly. Identification and classification of audio signals to species level is extremely desirable, but reliability can be highly affected by non-target noise, especially rainfall. Despite this demand, there are few easily applicable pre-processing methods available for rainfall detection for conservation practitioners and ecologists. Here, we use threshold values of two simple measures, Power Spectrum Density (amplitude) and Signal-to-Noise Ratio at two frequency bands, to differentiate between the presence and absence of heavy rainfall. We assess the effect of using different threshold values on Accuracy and Specificity. We apply the method to four datasets from both tropical and temperate regions, and find that it has up to 99% accuracy on tropical datasets (e.g. from the Brazilian Amazon), but performs less well in temperate environments. This is likely due to the intensity of rainfall in tropical forests and its falling on dense, broadleaf vegetation amplifying the sound. We show that by choosing between different threshold values, informed trade-offs can be made between Accuracy and Specificity, thus allowing the exclusion of large amounts of audio data containing rainfall in all locations without the loss of data not containing rain. We assess the impact of using different sample sizes of audio data to set threshold values, and find that 200 15 s audio files represents an optimal trade-off between effort, accuracy and specificity in most scenarios. This methodology and accompanying R package ‘hardRain’ is the first automated rainfall detection tool for pre-processing large acoustic datasets without the need for any additional rain gauge data

The Malaria Cell Atlas: single parasite transcriptomes across the complete Plasmodium life cycle

Malaria parasites adopt a remarkable variety of morphological life stages as they transition through multiple mammalian host and mosquito vector environments. We profiled the single-cell transcriptomes of thousands of individual parasites, deriving the first high-resolution transcriptional atlas of the entire life cycle. We then used our atlas to precisely define developmental stages of single cells from three different human malaria parasite species, including parasites isolated directly from infected individuals. The Malaria Cell Atlas provides both a comprehensive view of gene usage in a eukaryotic parasite and an open-access reference dataset for the study of malaria parasites