Integrating Biologging and Behavioral State Modeling to Identify Cryptic Behaviors and Post-capture Recovery Processes: New Insights From a Threatened Marine Apex Predator

Multisensor biologging provides a powerful tool for ecological research, enabling fine-scale observation of animals to directly link physiology and movement to behavior across ecological contexts. However, applied research into behavioral disturbance and recovery following human interventions (e.g., capture and translocation) has mostly relied on coarse location-based tracking or unidimensional approaches (e.g., dive profiles and activity/energetic metrics) that may not resolve behaviors and recovery processes. Biologging can improve insights into both disturbed and natural behavior, which is critical for management and conservation initiatives, although challenges remain in objectively identifying distinct behavioral modes from complex multisensor datasets. Using white sharks (Carcharodon carcharias) released from a non-lethal catch-and-release shark bite mitigation program, we explored how combining multisensor biologging (video, depth, accelerometers, gyroscopes, and magnetometers), track reconstruction and behavioral state modeling using hidden Markov models (HMMs) can improve our understanding of behavioral processes and recovery. Biologging tags were deployed on eight white sharks, recording their continuous behaviors, movements, and environmental context (habitat, interactions with other organisms/objects) for periods of 10–87 h post-release. Dive profiles and tailbeat analysis (as a standard, activity-based method for assessing recovery) indicated an immediate “disturbed” period of offshore movement, displaying rapid tailbeats and an average tailbeat-derived recovery period of 9.7 h, with evidence of smaller individuals having longer recoveries. However, further integrating magnetometer-derived headings, track reconstruction and HMM modeling revealed a cryptic shift to diurnal clockwise-counterclockwise circling behavior, which we argue represents compelling new evidence for hypothesized unihemispheric sleep amongst elasmobranchs. By simultaneously providing critical information toward conservation-focused shark management and understudied aspects of shark behavior, our study highlights how integrating multisensor information through HMMs can improve our understanding of both post-release and natural behavior, especially in species that are difficult to observe directly.

Disturbance and recovery: a synthesis of microbial community reassembly following disturbance across realms

Disturbances alter the diversity and composition of microbial communities, but whether microbiomes from different environments exhibit similar degrees of resistance or rates of recovery has not been evaluated. Here, we synthesized 86 time series of disturbed mammalian, aquatic, and soil microbiomes to examine how the recovery of microbial richness and community composition differed after disturbance. We found no general patterns in compositional variance (i.e., dispersion) in any microbiomes over time. Only mammalian microbiomes consistently exhibited decreases in richness following disturbance. Importantly, they tended to recover this richness, but not their composition, over time. In contrast, aquatic microbiomes tended to diverge from their pre-disturbance composition following disturbance. By synthesizing microbiome responses across environments, our study aids in the reconciliation of disparate microbial community assembly frameworks, and highlights the role of the environment in microbial community reassembly following disturbance.

Monitoring Mangrove Forest Degradation and Regeneration: Landsat Time Series Analysis of Moisture and Vegetation Indices at Rabigh Lagoon, Red Sea

Rabigh Lagoon, located on the eastern coast of the Red Sea, is an ecologically rich zone in Saudi Arabia, providing habitat to Avicennia marina mangrove trees. The environmental quality of the lagoon has been decaying since the 1990s mainly from sedimentation, road construction, and camel grazing. However, because of remedial measures, the mangrove communities have shown some degree of restoration. This study aims to monitor mangrove health of Rabigh Lagoon during the time it was under stress from road construction and after the road was demolished. For this purpose, time series of EVI (Enhanced Vegetation Index), MSAVI (Modified, Soil-Adjusted Vegetation Index), NDVI (Normalized Difference Vegetation Index), and NDMI (Normalized Difference Moisture Index) have been used as a proxy to plant biomass and indicator of forest disturbance and recovery. Long-term trend patterns, through linear, least square regression, were estimated using 30 m annual Landsat surface-reflectance-derived indices from 1986 to 2019. The outcome of this study showed (1) a positive trend over most of the study region during the evaluation period; (2) most trend slopes were gradual and weakly positive, implying subtle changes as opposed to abrupt changes; (3) all four indices divided the times series into three phases: degraded mangroves, slow recovery, and regenerated mangroves; (4) MSAVI performed best in capturing various trend patterns related to the greenness of vegetation; and (5) NDMI better identified forest disturbance and recovery in terms of water stress. Validating observed patterns using only the regression slope proved to be a challenge. Therefore, water quality parameters such as salinity, pH/dissolved oxygen should also be investigated to explain the calculated trends.