Biology subjects

Klinck, H.

Publications and source records attributed to Klinck, H..

6 recordsLinked to original sources

bioRxiv2025

Environmental DNA monitoring of waterfowl reveals community changes during migration

Large-scale visual surveys are an integral part of waterfowl conservation and management programs. In this paper we explore the possibility of using environmental DNA-based surveys as a cost-efficient, complementary tool to estimate populations of North American waterfowl species. To achieve this, we first evaluated the performance of all currently available avian metabarcoding primers and compared them to newly designed primers targeting the mitochondrial ND2 gene within the Anatidae tribes of North America. All the existing avian assays showed strong cross-priming amplification with other vertebrates. In contrast, in-silico analyses of our waterfowl targeted assays showed a high degree (>90%) of avian specificity, encompassing all the 132 Anatidae species sequenced thus far. We used this targeted metabarcoding approach to track the temporal variation in the relative abundance of waterfowl species during the fall migration at Montezuma National Wildlife Refuge, New York, a major resting area for waterfowl on their journey to and from North American nesting areas. We compared eDNA results with visual surveys conducted by us and from those reported on eBird. Our results showed that eDNA detected all waterfowl species (n= 25) during fall migration. Positive correlations existed between standardized eDNA read counts and the relative abundance of waterfowl species as reported in eBird on the day of sampling and up to five days prior. However, this approach did not provide a good metric for absolute abundance of waterfowl species: only 8 out of 25 waterfowl species showed significant correlations between the number of eDNA reads and the total abundance of birds. Overall, while eDNA-targeted metabarcoding has not yet been applied to study bird communities extensively, our results demonstrate that this technique can be used as an effective complementary tool for assessing species composition of waterfowl communities and estimating relative abundance of species within those communities.

genetics↗

bioRxiv2025

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales

Anthropogenic noise has increased ambient sound levels across the globe, both underwater and on land. Among its many negative impacts, heightened noise can impair communication in vocal animals through acoustic masking. Conceptually, noise reduces the animals communication space - the area in which an individual animal can effectively convey information to a conspecific listener. Previous studies have estimated the communication space using sound propagation models and/or behavioral studies. However, studies frequently equate signal recognition with signal detection - a necessary but not sufficient precondition - thereby persistently overestimating spatial coverage and underestimating anthropogenic impacts. Measuring communication is inherently difficult, and varies with taxa, call type, and context, leading to significant data gaps in key parameters. We propose that deep learning creates an opportunity to estimate biologically-relevant communication, even for data-limited species. In particular, we present a case study with the critically endangered North Atlantic right whale (Eubalaena glacialis; hereafter NARW). Prior research has demonstrated that the upcall - a low-frequency contact call produced across ages and sexes - encodes individual identity. We therefore consider a dataset of NARW vocalizations recorded with on-animal archival tags, spanning 234 samples across 11 individuals from 3 sites. First, we demonstrate that audio embeddings from the BirdNET model can robustly distinguish individual right whales. Then, we simulate the effect of varying ambient noise levels to estimate signal excess for both signal detection and individual identification. Altogether, we hope this work provides both a methodological advance for individual identification and a framework for better understanding anthropogenic impacts on vocal wildlife.

ecology↗

bioRxiv2024

Benchmarking for the automated detection and classification of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data

Recent advances in deep and transfer learning have revolutionized our ability for the automated detection and classification of acoustic signals from long-term recordings. Here, we provide a benchmark for the automated detection of southern yellow-cheeked crested gibbon (Nomascus gabriellae) calls collected using autonomous recording units (ARUs) in Andoung Kraleung Village, Cambodia. We compared the performance of support vector machines (SVMs), a quasi-DenseNet architecture (Koogu), transfer learning with pretrained convolutional neural network (ResNet50) models trained on the ImageNet dataset, and transfer learning with embeddings from a global birdsong model (BirdNET) based on an EfficientNet architecture. We also investigated the impact of varying the number of training samples on the performance of these models. We found that BirdNET had superior performance with a smaller number of training samples, whereas Koogu and ResNet50 models only had acceptable performance with a larger number of training samples (>200 gibbon samples). Effective automated detection approaches are critical for monitoring endangered species, like gibbons. It is unclear how generalizable these results are for other signals, and future work on other vocal species will be informative. Code and data are publicly available for future benchmarking.

ecology↗

bioRxiv2022

Is there an accurate and generalisable way to use soundscapes to monitor biodiversity?

Acoustic monitoring has the potential to deliver biodiversity insight on vast scales. Whilst autonomous recording networks are being deployed across the world, existing analytical techniques struggle with generalisability. This limits the insight that can be derived from audio recordings in regions without ground-truth calibration data. By calculating 128 learned features and 60 soundscape indices of audio recorded during 8,023 avifaunal point counts from diverse ecosystems, we investigated the generalisability of soundscape approaches to biodiversity monitoring. Within each dataset, we found univariate correlations between several acoustic features and avian species richness, but features behaved unpredictably across datasets. Training a machine learning model on compound indices, we could predict species richness within datasets. However, models were uninformative when applied to datasets not used for training. We found that changes in soundscape features were correlated with changes in avian communities across all datasets. However, there were cases where avian communities changed without an associated shift in soundscapes. Our results suggest that there are no common hallmarks of biodiverse soundscapes across ecosystems. Therefore, soundscape monitoring should only be used when high quality ground-truth data exists for the region of interest, and in conjunction with more targeted and accurate in-person ecological surveys. By better understanding how to use interpret data reliably, we hope to unlock the scale at which acoustic monitoring can be used to deliver true impact for land managers and scientists monitoring biodiversity around the world. SummaryWhilst eco-acoustic monitoring has the potential to deliver biodiversity insight on vast scales, existing analytical approaches behave unpredictably across studies. We collated 8,023 audio recordings with paired manual avifaunal point counts to investigate whether soundscapes could be used to monitor biodiversity across diverse ecosystems. We found that neither univariate indices nor machine learning models were predictive of species richness across datasets, but soundscape change was consistently indicative of community change. Our findings indicate that there are no common features of biodiverse soundscapes, and that soundscape monitoring should be used cautiously and in conjunction with more reliable in-person ecological surveys.

ecology↗

bioRxiv2021

Simultaneous acoustic monitoring uncovers evidence of biodiversity loss and overlooked temporal variation in a threatened Amazonian bird community

The vocal activity and detectability of tropical birds are subject to high levels of temporal heterogeneity, but quantifying patterns of diel and day-to-day variation in complex systems is challenging with traditional point count methods. As a result, research concerning stochastic temporal effects on tropical avian assemblages is limited, typically offering only broad conclusions, e.g., overall activity is highest in the first few hours of the morning and some species are active at different times of the day. Passive acoustic monitoring introduces several advantages for studying temporal variation, particularly by enabling simultaneous and continuous data collection across adjacent sites. Here, we employed autonomous recording units to quantify temporal variation in avian vocal activity and observed species richness at an Amazonian reserve in Madre de Dios, Peru--a region featuring some of Earths richest, most complex avian assemblages. We manually annotated 18 dawn hour recordings, collected simultaneously from three separate days at the same six sites, which represent various microhabitats and avian community compositions. We documented significant and consistent temporal variation in avian vocal activity levels and observed species richness within the dawn hour and across days. We found that temporal effects were stronger for vocal activity than for observed species richness and that vocal activity patterns over the course of the dawn hour varied on a species-to-species basis. Our results indicate that overlooked temporal variation in Amazonian soundscapes may obfuscate the results of surveys that fail to sufficiently account for temporal variables with simultaneous monitoring. While manual analysis of large volumes of soundscape data remains challenging, such data should be collected as a supplement to traditional surveys whenever possible. Rapidly advancing research concerning the automated processing of acoustic data could lead to more efficient methods for reducing temporal bias and improving the calibration and accuracy of ornithological surveys in the tropics.

ecology↗

bioRxiv2019

Combining machine learning and a universal acoustic feature-set yields efficient automated monitoring of ecosystems

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labour-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we developed a generalisable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed ecosystem soundscapes from a wide variety of biomes into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, paving the way for real-time detection of irregular environmental behaviour including illegal activity. Our highly generalisable approach, and the common set of features, will enable scientists to unlock previously hidden insights from eco-acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts.

ecology↗