Assessing forest degradation using multivariate and machine-learning methods in the Patagonian temperate rain forest.

The process of forest degradation, along with deforestation, is the second greatest producer of global greenhouse gas emissions. A key challenge that remains unresolved is how to quantify the critical threshold that distinguishes a degraded from a non-degraded forest. We determined the critical threshold of forest degradation in mature stands belonging to the temperate evergreen rain forest of southern Chile by quantifying key forest stand factors characterizing the forest degradation status. Forest degradation in this area is mainly caused by high grading, harvesting of fuelwood, and sub-canopy grazing by livestock. We established 160 500-m2 plots in forest stands that represented varied degrees of alteration (from pristine conditions to obvious forest degradation), and measured several variables related to the structure and composition of the forest stands, including exotic and native species richness, soil nutrient levels, and other landscape-scale variables. In order to identify classes of forest degradation, we applied multivariate and machine-learning analyses. We found that richness of exotic species (including invasive species) with a diameter at breast height (DBH) < 10 cm and tree density (N, DBH > 10 cm) were the two composition and structural variables that best explained the forest degradation status, e.g., forest stands with five or more exotic species were consistently found more associated with degraded forest and stands with N < 200 trees/ha represented degraded forests, while N > 1,000 trees/ha represent pristine forests. We introduced an analytical methodology, mainly based on machine learning, that successfully identified the forest degradation status that can be replicated in other scenarios. In conclusion, here by providing an extensive data set quantifying forest and site attributes, the results of this study are undoubtedly useful for managers and decision makers in classifying and mapping forests suffering various degrees of degradation.

Divergence in floral trait preferences between nonflower-specialized birds and insects on the Galápagos.

PREMISE OF THE STUDY: The characteristic scarcity of insects on remote oceanic islands has driven nonflower-specialized vertebrates to broaden their trophic niches and explore floral resources. From our previous studies in the Galápagos, we know that native insectivorous and frugivorous birds visit a wide range of entomophilous flowers and can also act as effective pollinators. Here, we tested whether opportunistic Galápagos birds show any preference for specific floral traits, and if so, this preference differs from that of insects. METHODS: Sixteen floral morphology and nectar traits of 26 native species were studied, as well as the frequency with which they are visited by birds and insects. Nonmetric multidimensional scaling (NMDS) was used to evaluate the distribution of flower traits values along two main dimensions and measure the similarity between the plants visited mostly by birds versus those by insects. KEY RESULTS: NMDS of floral traits resulted in two species groups: (1) bell-shaped, white flowers with wider corollas at nectary level and higher nectar volume, associated with high bird visitation rates; and (2) bowl and tubular-shaped flowers with narrower corollas at nectary level and lower nectar volume, associated with high insect visitation rates. CONCLUSIONS: Despite the divergence in floral trait preferences between opportunistic Galápagos birds and insects, bird-visited flowers display mixed traits not fitting the classical ornithophilous syndrome. This finding is compatible with the existence of a transitional or bet-hedging phenotype between insect and bird visitors and underscores the importance of coevolution and floral diversification in nonspecialized plant-visitor interactions.