Retention forestry aims to mitigate impacts of native forestry on biodiversity, but data are limited on its effectiveness for threatened species. We used acoustics to investigate the resilience of a folivorous marsupial, the koala Phascolarctos cinereus, to timber harvesting where a key mitigation practice is landscape exclusion of harvesting. We deployed acoustic recorders at 171 sites to record male bellows (~14,640 hours) for use in occupancy modelling and for comparisons of bellow rate (bellows night-1). Surveys targeted modelled medium-high quality habitat, with sites stratified by time since logging and logging intensity, including old growth as a reference. After scanning recordings with software to identify koala bellows, we found a high probability of detection (~0.45 per night), but this varied with minimum temperature and recorder type. Naïve occupancy was ~ 64% across a broad range of forests, which was at least five times more than expected based on previous surveys using alternative methods. After accounting for imperfect detection, probability of occupancy was influenced by elevation (-ve), cover of important browse trees (+ve), landscape NDVI (+ve) and extent of recent wildfire (-ve, but minor effect). Elevation was the most influential variable, though the relationship was non-linear and low occupancy was most common at tableland elevations (> 1000 m). Neither occupancy nor bellow rate were influenced by timber harvesting intensity, time since harvesting or local landscape extent of harvesting or old growth. Extrapolation of occupancy across modelled habitat indicates that the hinterland forests of north-east NSW support a widespread, though likely low density koala population that is considerably larger than previously estimated. Retention forestry has a significant role to play in mitigating harvesting impacts on biodiversity, including for forest specialists, but localised studies are needed to optimise prescriptions for koalas.

Species distribution models have great potential to efficiently guide management for threatened species, especially for those that are rare or cryptic. We used MaxEnt to develop a regional‐scale model for the koala Phascolarctos cinereus at a resolution (250 m) that could be used to guide management. To ensure the model was fit for purpose, we placed emphasis on validating the model using independently‐collected field data. We reduced substantial spatial clustering of records in coastal urban areas using a 2‐km spatial filter and by modeling separately two subregions separated by the 500‐m elevational contour. A bias file was prepared that accounted for variable survey effort. Frequency of wildfire, soil type, floristics and elevation had the highest relative contribution to the model, while a number of other variables made minor contributions. The model was effective in discriminating different habitat suitability classes when compared with koala records not used in modeling. We validated the MaxEnt model at 65 ground‐truth sites using independent data on koala occupancy (acoustic sampling) and habitat quality (browse tree availability). Koala bellows (n = 276) were analyzed in an occupancy modeling framework, while site habitat quality was indexed based on browse trees. Field validation demonstrated a linear increase in koala occupancy with higher modeled habitat suitability at ground‐truth sites. Similarly, a site habitat quality index at ground‐truth sites was correlated positively with modeled habitat suitability. The MaxEnt model provided a better fit to estimated koala occupancy than the site‐based habitat quality index, probably because many variables were considered simultaneously by the model rather than just browse species. The positive relationship of the model with both site occupancy and habitat quality indicates that the model is fit for application at relevant management scales. Field‐validated models of similar resolution would assist in guiding management of conservation‐dependent species.

There is global concern about tropical forest degradation, in part, because of the associated loss of biodiversity. Communities and indigenous people play a fundamental role in tropical forestPalabras Clave: agricultura de sustento, avifauna, bioacústica, biodiversidad vocal, caza, degradación del bosque, manejo comunitario del bosque, planeación del uso de suelo

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Long-duration recordings of the natural environment have many advantages in passive monitoring of animal diversity. Technological advances now enable the collection of far more audio than can be listened to, necessitating the development of scalable approaches for distinguishing signal from noise. Computational methods, using automated species recognisers, have improved in accuracy but require considerable coding expertise. The content of environmental recordings is unconstrained, and the creation of labelled datasets required for machine learning purposes is a time-consuming, expensive enterprise. Here, we describe a visual approach to the analysis of environmental recordings using long-duration false-colour (LDFC) spectrograms, prepared from combinations of spectral indices. The technique was originally developed to visualize 24-hour “soundscapes.” A soundscape is an ecoacoustics concept that encompasses the totality of sound in an ecosystem. We describe three case studies to demonstrate how LDFC spectrograms can be used, not only to study soundscapes, but also to monitor individual species within them. In the first case, LDFC spectrograms help to solve a “needle in the haystack” problem—to locate vocalisations of the furtive Lewin’s Rail (Tasmanian), Lewinia pectoralis brachipus. We extend the technique by using a machine learning method to scan multiple days of LDFC spectrograms. In the second case study, we demonstrate that frog choruses are easily identified in LDFC spectrograms because of their extended time-scale. Although calls of individual frogs are lost in the cacophony of sound, spectral indices can distinguish different chorus characteristics. Third, we demonstrate that the method can be extended to the detection of bat echolocation calls. By converting complex acoustic data into readily interpretable images, our practical approach bridges the gap between bioacoustics and ecoacoustics, encompassing temporal scales across three orders of magnitude. Using the one methodology, it is possible to monitor entire soundscapes and individual species within those soundscapes.