The global increase in observed forest dieback, characterized by the death of tree foliage, heralds widespread decline in forest ecosystems. This degradation causes significant changes to ecosystem services and functions, including habitat provision and carbon sequestration, which can be difficult to detect using traditional monitoring techniques, highlighting the need for large-scale and high-frequency monitoring. Contemporary developments in the instruments and methods to gather and process data at large scales mean this monitoring is now possible. In particular, the advancement of low-cost drone technology and deep learning on consumer-level hardware provide new opportunities. Here, we use an approach based on deep learning and vegetation indices to assess crown dieback from RGB aerial data without the need for expensive instrumentation such as LiDAR. We use an iterative approach to match crown footprints predicted by deep learning with field-based inventory data from a Mediterranean ecosystem exhibiting drought-induced dieback, and compare expert field-based crown dieback estimation with vegetation index-based estimates. We obtain high overall segmentation accuracy (mAP: 0.519) without the need for additional technical development of the underlying Mask R-CNN model, underscoring the potential of these approaches for non-expert use and proving their applicability to real-world conservation. We also find that color-coordinate based estimates of dieback correlate well with expert field-based estimation. Substituting ground truth for Mask R-CNN model predictions showed negligible impact on dieback estimates, indicating robustness. Our findings demonstrate the potential of automated data collection and processing, including the application of deep learning, to improve the coverage, speed, and cost of forest dieback monitoring.

The brown cocoa mirid, Sahlbergella singularis is the most important insect pest of cocoa for field damage has been classified into three categories that include blast, stagheadedness and mirid pocket. The mirid attack predisposes the cocoa plant to secondary infection by opportunistic pathogenic fungi. This study evaluated the dieback conditions associated with mirid damage due to associated fungi from lesions on pods, twigs and pin punctures. The relationship between Phytophthora black pod infection and mirid infestation in the field was also established. The results indicated that dieback progression from the mirid lesions (8.5 mm) was significantly different (P< 0.05) from that of pin punctures (7.8 mm). Lasiodiplodia theobromae had the highest proportion of isolates from pods (33%), twigs (47%), and pin punctures (38%). Fusarium decemcellulare (Albonectria rigidiuscula) accounted for 27.8% and 31.6% from pods and twigs; however, it was not recovered from pin punctures. Other isolated pathogens were Aspergillus species and Rhizoctonia species. There was a significantly strong positive correlation coefficient of 0.74 between the mirid population and the black pod disease infection in the field. The major fruit-bearing season of cocoa witnessed an increase in mirid infestation and blackpod infection. The effect of mirid–pathogenic fungi-associated damage could be ameliorated through effective crop husbandry practices to ensure that cocoa plant tissues are not unduly exposed to biological and mechanical injuries.

Hemiepiphytic plants grow for part of their life as true epiphytes, then become terrestrial through the production of aerial roots that grow from the canopy to the ground. Long-term measurement of growth, dieback and mortality of aerial roots of hemiepiphytic plants in a lower montane moist tropical forest in western Panama was used to elucidate life-history strategies of hemiepiphytes from two families. The fates of 156 aerial roots of five species of Clusiaceae and Araceae were followed for 10 mo. Some roots were cut to experimentally study the effect of injury on resprouting and survival. Aerial roots of Araceae grew more than twice as fast as those of Clusiaceae but had a much greater mortality rate. Roots of both families grew much faster during the wet than dry season. Even for the fastest growing roots, growth and survival models suggest that only 18% of Araceae roots were likely to survive long enough to reach the ground from a branch 10 m high, whereas 87% of roots of Clusiaceae were likely to do so. This suggests that only those Araceae hemiepiphytes that produce a large number of aerial roots or are located close to the ground are likely to reach the soil.