Vessel traffic influences distribution of Aotearoa New Zealand's endemic dolphin (Cephalorhynchus hectori)

Cetacean tourism and vessel traffic have grown considerably around the world in recent decades. At Akaroa Harbor, Aotearoa New Zealand, recreational vessel traffic, dolphin tourism, and cruise ship presence increased substantially between 2008 and 2020. We examined the relationship between vessel traffic parameters and the presence of Hector's dolphins (Cephalorhynchus hectori) during the austral summer 2019–2020, using automated vessel tracking and autonomous passive acoustic monitoring. Data were collected between December 2019 and May 2020, including the entirety of the first COVID‐19 nationwide lockdown. Generalized additive models revealed that increasing levels of motor vessel traffic, the presence of cruise ships, and high levels of dolphin tour vessel traffic resulted in decreases in acoustic detections of dolphins. Our findings suggest that Hector's dolphins at Akaroa Harbor were displaced from core habitat in response to each of these vessel traffic parameters. We recommend that managers use immediately actionable tools to reduce the impacts of vessels on these dolphins.

Diversity Monitoring of Coexisting Birds in Urban Forests by Integrating Spectrograms and Object-Based Image Analysis

In the context of rapid urbanization, urban foresters are actively seeking management monitoring programs that address the challenges of urban biodiversity loss. Passive acoustic monitoring (PAM) has attracted attention because it allows for the collection of data passively, objectively, and continuously across large areas and for extended periods. However, it continues to be a difficult subject due to the massive amount of information that audio recordings contain. Most existing automated analysis methods have limitations in their application in urban areas, with unclear ecological relevance and efficacy. To better support urban forest biodiversity monitoring, we present a novel methodology for automatically extracting bird vocalizations from spectrograms of field audio recordings, integrating object-based classification. We applied this approach to acoustic data from an urban forest in Beijing and achieved an accuracy of 93.55% (±4.78%) in vocalization recognition while requiring less than ⅛ of the time needed for traditional inspection. The difference in efficiency would become more significant as the data size increases because object-based classification allows for batch processing of spectrograms. Using the extracted vocalizations, a series of acoustic and morphological features of bird-vocalization syllables (syllable feature metrics, SFMs) could be calculated to better quantify acoustic events and describe the soundscape. A significant correlation between the SFMs and biodiversity indices was found, with 57% of the variance in species richness, 41% in Shannon’s diversity index and 38% in Simpson’s diversity index being explained by SFMs. Therefore, our proposed method provides an effective complementary tool to existing automated methods for long-term urban forest biodiversity monitoring and conservation.