The management of animal endangered species requires detailed information on their distribution and abundance, which is often hard to obtain. When animals communicate using sounds, one option is to use automatic sound recorders to gather information on the species for long periods of time with low effort. One drawback of this method is that processing all the information manually requires large amounts of time and effort. Our objective was to create a relatively “user‐friendly” (i.e., that does not require big programming skills) automatic detection algorithm to improve our ability to get basic data from sound‐emitting animal species. We illustrate our algorithm by showing two possible applications with the Hawai'i ‘Amakihi, Hemignathus virens virens, a forest bird from the island of Hawai'i. We first characterized the ‘Amakihi song using recordings from areas where the species is present in high densities. We used this information to train a classification algorithm, the support vector machine (SVM), in order to identify ‘Amakihi songs from a series of potential songs. We then used our algorithm to detect the species in areas where its presence had not been previously confirmed. We also used the algorithm to compare the relative abundance of the species in different areas where management actions may be applied. The SVM had an accuracy of 86.5% in identifying ‘Amakihi. We confirmed the presence of the ‘Amakihi at the study area using the algorithm. We also found that the relative abundance of ‘Amakihi changes among study areas, and this information can be used to assess where management strategies for the species should be better implemented. Our automatic song detection algorithm is effective, “user‐friendly” and can be very useful for optimizing the management and conservation of those endangered animal species that communicate acoustically.

Most Hawaiian forests lack resiliency following disturbance due to the presence of non‐native and invasive plant and animal species. The montane wet forest within Hakalau Forest National Wildlife Refuge on Hawai'i island has a long history of ungulate disturbance but portions of the refuge were fenced and most ungulates excluded by the early 1990s. We examined patterns of regeneration within two 100 ha study sites in this forest following the removal of ungulates and in the absence of invasive woody tree species to determine, in part, if passive restoration techniques can be successful under these conditions. We characterized growth, mortality, and basal area (BA) changes for approximately 7,100 marked individuals of all native tree species present in two surveys over a 17–18‐year period within two hundred 30 m diameter forest plots. Considerable recruitment within plots of new trees of all species significantly changed size class distributions and erased deficits in small‐sized trees observed during the first survey, particularly for the codominant canopy tree, koa (Acacia koa). Overall, growth of established dominant 'ōhi'a trees (Metrosideros polymorpha) and recruitment of mid‐canopy trees contributed to increases in BA while high levels of mortality for large A. koa trees contributed to decreased BA. This resulted in a slight increase in BA between the two surveys (+1.9%). This study demonstrates that fencing and ungulate removal may have rescued the A. koa population by facilitating the first real pulse in recruitment in over a century, and that passive restoration can be a successful management strategy in this forest.

Little is known about how important social behaviors such as song vary within and among populations for any of the endemic Hawaiian honeycreepers. Habitat loss and non‐native diseases (e.g., avian malaria) have resulted in isolation and fragmentation of Hawaiian honeycreepers within primarily high elevation forests. In this study, we examined how isolation of Hawai'i ‘amakihi (Chlorodrepanis virens) populations within a fragmented landscape influences acoustic variability in song. In the last decade, small, isolated populations of disease tolerant ‘amakihi have been found within low elevation forests, allowing us to record ‘amakihi songs across a large elevational gradient (10–1800 m) that parallels disease susceptibility on Hawai'i island. To understand underlying differences among populations, we examined the role of geographic distance, elevation, and habitat structure on acoustic characteristics of ‘amakihi songs. We found that the acoustic characteristics of ‘amakihi songs and song‐type repertoires varied most strongly across an elevational gradient. Differences in ‘amakihi song types were primarily driven by less complex songs (e.g., fewer frequency changes, shorter songs) of individuals recorded at low elevation sites compared to mid and high elevation populations. The reduced complexity of ‘amakihi songs at low elevation sites is most likely shaped by the effects of habitat fragmentation and a disease‐driven population bottleneck associated with avian malaria, and maintained through isolation, localized song learning and sharing, and cultural drift. These results highlight how a non‐native disease through its influence on population demographics may have also indirectly played a role in shaping the acoustic characteristics of a species.