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SUMMARYIn the Cardamom Ranges (Cambodia) community-based natural resource management (CBNRM) is proposed by the international non-governmental organizations (NGOs) community as a natural resource management strategy to achieve the targeted outcomes associated with the protected area (PA) management plan. Local people are expected to participate in CBNRM projects such as community forestry (CF) in order that the protected area management plan can be realized. The experiences of the local people are juxtaposed against the aims of these local biodiversity projects. Overall, it is accepted by the NGOs and government agencies that communities need to be involved in the design and management of the PA and that the protection of biodiversity resources can only occur with the provision of alternatives for local livelihood options to decrease land clearing for agriculture and harvesting of wild foods and animals. This case points to a basic misalignment between biodiversity conservation and CBNRM. Participants in this study contested the meaning and usefulness of the PA and the CF projects. Their concerns were cultural, social, economic and political, exposing uneven relations of power and uncertainty associated with the long term outcomes. Participation itself required scrutiny in this situation, as did the promotion of a global biodiversity ‘good’ over local understandings of place and landscape. Lessons from more than 20 years of participatory CBNRM may be used to reconfigure the CBNRM ideal, to assist planners and implementers towards an integrated approach with biodiversity values reflected in both conservation and local production systems, acknowledging that these systems are culturally constituted.

Passive acoustic detectors are increasingly used for monitoring biodiversity, particularly for echolocating bat species (Microchiroptera). However, identification of calls collected at large scales is hindered by substantial variation within and between species, and the considerable time investment needed to manually identify acoustic data. We use acoustic data from 14 species of echolocating bats, occurring in temperate forests and woodlands of southeastern Australia to build a supervised classification model that identifies species from large acoustic datasets. Acoustic data from hand‐release (39,567) and free‐flying (8851) bat calls were used to build a predictive model, which was then validated using field‐collected calls (149,097) from the same region. We maximized the model fit per species by validating the associated confidence scores against manually identified presence and absence values. This allowed us to model the identification success of each species as a function of the confidence score. From this relationship, we set specific thresholds for accepting species identification, enabling more accurate classification of calls and identification of multiple bat species within a single acoustic recording. Including calls from manually identified free‐flying bats improved overall identification accuracy, including a 60% improvement for bats that navigate in open spaces. Assigning species‐specific thresholds achieved substantial improvements in overall model confidence, with functionally meaningful changes in the identification of species exhibiting considerable acoustic overlap in time and frequency measures. Research into the ecological requirements of species is hampered by problems with identification. Our research illustrates that internal train–test validation overestimates model accuracy particularly for species that were in low abundance or for uncommon species, which are acoustically similar to more common ones. Recognizing this, we set specific thresholds per species below which identifications were not accepted. Our method is particularly relevant in locations with high overlap in species' call parameters, which can result in false negatives in preference for species that are easier to identify because of the common practice of assigning one species per acoustic recording. This research proposes a cautious method to substantially reduce the burden of manual identification of large acoustic datasets.