Conservation potential of shade-tolerant forest species in agricultural mosaics in the eastern Brazilian Amazon

Shade-tolerant forest species are among the most susceptible to habitat loss in agricultural mosaics, where a variety of croplands is connected to forests at different levels of anthropogenic disturbance. We aimed to evaluate the community similarity of shade-tolerant species among different land use types across agricultural mosaics with different levels of disturbance. The study was conducted in three municipalities in southern and southeastern Pará state, in eastern Amazonia. A multiple-community similarity measure based on the Horn similarity index was used to compare land use types and assess the resilience of shade-tolerant species towards forest loss and disturbance at the landscape level. High shade-tolerant species similarity was found between mature forest fragments that underwent different levels of disturbance in all three agricultural mosaics, but secondary forests had lower similarity with mature forest in the most fragmented and altered mosaic. Shade-tolerant species showed very low density in croplands, but the same group of species seemed to colonize agricultural fields of annual crops and clean pasture, as they showed high community similarity. Another group of species was present in invaded pastures, probably due to the effects of time since land abandonment after woody species colonization. Mixed tree plantations were more similar to mature and secondary forests than other types of croplands. Shade-tolerant species similarity was higher among land use types inserted in agricultural landscapes that maintained conserved forest fragments. Our results suggest that the conservation of mature forests and landscape connectivity are crucial to the maintenance of shade-tolerant species in agricultural mosaics. (AU)

Estimating babassu palm density using automatic palm tree detection with very high spatial resolution satellite images.

High spatial resolution images as well as image processing and object detection algorithms are recent technologies that aid the study of biodiversity and commercial plantations of forest species. This paper seeks to contribute knowledge regarding the use of these technologies by studying randomly dispersed native palm tree. Here, we analyze the automatic detection of large circular crown (LCC) palm tree using a high spatial resolution panchromatic GeoEye image (0.50 m) taken on the area of a community of small agricultural farms in the Brazilian Amazon. We also propose auxiliary methods to estimate the density of the LCC palm tree Attalea speciosa (babassu) based on the detection results. We used the "Compt-palm" algorithm based on the detection of palm tree shadows in open areas via mathematical morphology techniques and the spatial information was validated using field methods (i.e. structural census and georeferencing). The algorithm recognized individuals in life stages 5 and 6, and the extraction percentage, branching factor and quality percentage factors were used to evaluate its performance. A principal components analysis showed that the structure of the studied species differs from other species. Approximately 96% of the babassu individuals in stage 6 were detected. These individuals had significantly smaller stipes than the undetected ones. In turn, 60% of the stage 5 babassu individuals were detected, showing significantly a different total height and a different number of leaves from the undetected ones. Our calculations regarding resource availability indicate that 6870 ha contained 25,015 adult babassu palm tree, with an annual potential productivity of 27.4 t of almond oil. The detection of LCC palm tree and the implementation of auxiliary field methods to estimate babassu density is an important first step to monitor this industry resource that is extremely important to the Brazilian economy and thousands of families over a large scale.