Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant

In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa. Earth's biodiversity is increasingly under threat by sources of anthropogenic change (e.g. resource extraction, land use change, and climate change) and surveying animal populations is critical for developing conservation strategies. However, manually monitoring tropical forests or deep oceans is intractable. For species that communicate acoustically, researchers have argued for placing audio recorders in the habitats as a cost-effective and non-invasive method, a strategy known as passive acoustic monitoring (PAM). In collaboration with conservation efforts, we construct a large labeled dataset of passive acoustic recordings of the African Forest Elephant via crowdsourcing, compromising thousands of hours of recordings in the wild. Using state-of-the-art techniques in artificial intelligence we improve upon previously proposed methods for passive acoustic monitoring for classification and segmentation. In real-time detection of elephant calls, network bandwidth quickly becomes a bottleneck and efficient ways to compress the data are needed. Most audio compression schemes are aimed at human listeners and are unsuitable for low-frequency elephant calls. To remedy this, we provide a novel end-to-end differentiable method for compression of audio signals that can be adapted to acoustic monitoring of any species and dramatically improves over naive coding strategies

Characterising soundscapes across diverse ecosystems using a universal acoustic feature set

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed a generalizable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed soundscapes from a variety of ecosystems 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, providing a potential route for real-time automated detection of irregular environmental behavior including illegal logging and hunting. Our highly generalizable approach, and the common set of features, will enable scientists to unlock previously hidden insights from acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts

Long-term monitoring of Dzanga Bai forest elephants: forest clearing use patterns.

Individual identification of the relatively cryptic forest elephant (Loxodonta cyclotis) at forest clearings currently provides the highest quality monitoring data on this ecologically important but increasingly threatened species. Here we present baseline data from the first 20 years of an individually based study of this species, conducted at the Dzanga Clearing, Central African Republic. A total of 3,128 elephants were identified over the 20-year study (1,244 adults; 675 females, 569 males). It took approximately four years for the majority of elephants visiting the clearing to be identified, but new elephants entered the clearing every year of the study. The study population was relatively stable, varying from 1,668 to 1,864 individuals (including juveniles and infants), with increasingly fewer males than females over time. The age-class distribution for females remained qualitatively unchanged between 1995 and 2010, while the proportion of adult males decreased from 20% to 10%, likely reflecting increased mortality. Visitation patterns by individuals were highly variable, with some elephants visiting monthly while others were ephemeral users with visits separated by multiple years. The number of individuals in the clearing at any time varied between 40 and 100 individuals, and there was little evidence of a seasonal pattern in this variation. The number of elephants entering the clearing together (defined here as a social group) averaged 1.49 (range 1-12) for males and 2.67 (range 1-14) for females. This collation of 20 years of intensive forest elephant monitoring provides the first detailed, long term look at the ecology of bai visitation for this species, offering insight to the ecological significance and motivation for bai use, social behavior, and threats to forest elephants. We discuss likely drivers (rainfall, compression, illegal killing, etc.) influencing bai visitation rates. This study provides the baseline for future demographic and behavioral studies of this population

Demography of a forest elephant population

<div><p>African forest elephants face severe threats from illegal killing for ivory and bushmeat and habitat conversion. Due to their cryptic nature and inaccessible range, little information on the biology of this species has been collected despite its iconic status. Compiling individual based monitoring data collected over 20 years from the Dzanga Bai population in Central African Republic, we summarize sex and age specific survivorship and female age specific fecundity for a cohort of 1625 individually identified elephants. Annual mortality (average = 3.5%) and natality (average = 5.3%) were lower and markedly less variable relative to rates reported for savanna elephant populations. New individuals consistently entered the study system, leading to a 2.5% average annual increase in the registered population. Calf sex ratios among known birth did not differ from parity. A weak seasonal signal in births was detected suggesting increased conceptions during the wet season. Inter-calf intervals and age of primiparity were longer relative to savanna elephant populations. Within the population, females between the ages of 25–39 demonstrated the shortest inter-calf intervals and highest fecundity, and previous calf sex had no influence on the interval. Calf survivorship was high (97%) the first two years after birth and did not differ by sex. Male and female survival began to differ by the age of 13 years, and males demonstrated significantly lower survival relative to females by the age of 20. It is suspected these differences are driven by human selection for ivory. Forest elephants were found to have one of the longest generation times recorded for any species at 31 years. These data provide fundamental understanding of forest elephant demography, providing baseline data for projecting population status and trends.</p></div

Annual Dzanga demography and population trend from 1997 to 2010.

<p>Net inflows (births, dark blue; immigration, light blue) were higher than net losses (death, dark brown; dispersal, light brown) in most years.</p

Annual size of the population visiting Dzanga Bai.

<p>(A) Estimates of the total population (individuals of all ages, including immigrants and those born into the population as of the end of each study year). (B) Estimates of the core population. Solid curves include all individuals known alive in that year, whether observed in the bai or not. Dashed curves include only individuals actually observed, adjusted for the number of observation days in each year.</p

Female (blue) and male (red) survivorship through the first 20 years of age, estimated using cohort analyses of offspring with age accuracy of 3 months or better.

<p>Shaded areas are the point-wise 95% confidence interval on the survival probability.</p