Forest ecosystems face increasing threats from climate change, resource exploitation, and other anthropogenic disturbances causing biodiversity loss and habitat fragmentation. The conservation priority of connected, healthy forests necessitates robust monitoring that covers the landscape, ecosystem, species, and genetic levels and employs direct as well as indirect methods. Connectivity objectives encompass patch colonization, prioritization, and landscape assessment at multiple scales. Monitoring landscapes and forest ecosystems involves assessing their physical attributes and functional diversity to understand biodiversity, land-use changes, and threats like deforestation and climate impacts. Remote sensing offers large-scale data collection, while terrestrial surveys including laser scanning provide detailed insights into forest dynamics. Challenges include scale issues, standardization, and potential oversights in finer-scale variations. While species monitoring captures long-term shifts in abundance or distribution, it can be resource-intensive and challenging for elusive species. Alternatively, molecular methods such as the use of environmental DNA (eDNA) can be effective for community monitoring, with DNA analysis being particularly effective for detecting the presence of endangered or elusive organisms and providing spatial and temporal high-resolution data for effective conservation and management. Gene-based monitoring traces changes in individual species’ genetic parameters over time. Genetic indicators, which have recently been included in biodiversity monitoring standards, provide essential insights into connectivity and adaptive capacity. Landscape genetics combines conservation genetics and ecology to understand gene flow barriers and facilitators: population synchrony signals functional connectivity. Although genetic monitoring demands great technical expertise, it is less time-consuming than conventional methods. For future forest connectivity monitoring, a combination of various approaches is conceivable. Existing connectivity indicators need rigorous evaluation in terms of their sensitivity to environmental impacts. Dynamic models and novel indicators along with data sharing and collaboration will be crucial for future efforts in connectivity monitoring.
