The Brazilian Amazon has been a focus of land development with large swaths of forests converted to agriculture. Forest degradation by selective logging and fires has accompanied the advance of the frontier and has resulted in significant impacts on Amazonian ecosystems. Changes in forest structure resulting from forest disturbances have large impacts on the surface energy balance, including on land surface temperature (LST) and evapotranspiration (ET). The objective of this study is to assess the effects of forest disturbances on water fluxes and canopy structural properties in a transitional forest site located in Mato Grosso State, Southern Amazon. We used ET and LST products from MODIS and Landsat 8 as well as GEDI-derived forest structure data to address our research questions. We found that disturbances induced seasonal water stress, more pronounced and earlier in croplands and pastures than in forests, and more pronounced in second-growth and recently burned areas than in logged and intact forests. Moreover, we found that ET and LST were negatively related, with a more consistent relationship across disturbance classes in the dry season than the wet season, and that forest and cropland and pasture classes showed contrasting relationships in the dry season. Finally, we found that canopy structural properties exhibited moderate relationships with ET and LST in the most disturbed forests, but negligible correlations in the least disturbed forests. Our findings help to elucidate degraded forests functioning under a changing climate and to improve estimates of water and energy fluxes in the Amazon degraded forests.

Species Distribution Models (SDMs) are commonly used statistical tools in conservation biology, global change assessment, and reserve prioritization. Correlative SDMs relate species occurrences to environmental conditions, and it is common to model heterogeneity in the data with coarse-scale spatial and temporal predictors. However, this approach neglects the fine-scale environmental conditions experienced by most organisms. Further, most SDMs use occurrence data from short-term studies but make long-term predictions of future conditions. We compare four modeling frameworks that varied the temporal extent (short-term [1 year] versus long-term [10 years]) and resolution of environmental data (fine versus coarse). We expected that long-term data and finer temporal resolution of environmental variables would provide more accurate model predictions because they integrate variability in population sizes under varying microclimatic conditions. We built SDMs for 37 bird species in the H. J. Andrews Experimental Forest, Cascade Range, Oregon (USA). We used a 10-year (2010-2019) time series of annual observations during breeding season across 184 sites as response variables and gridded maps of hourly below forest canopy microclimate temperatures and LiDAR-derived vegetation variables as predictors. We evaluated the interannual transferability of long- versus short-term models and fine versus coarse-resolution temperature models; we also tested whether species’ functional traits affected the performance of models. Temporally dynamic (long-term) models with higher-resolution microclimate data outperformed static and short-term approaches in terms of performance (AUC difference ~ 0.10, TSS difference ~ 0.12). Model performance and similarity between spatial predictions were higher for dynamic rather than static models, especially for migratory species. Models for small bird species performed better as temporal resolution increased, whereas for long-lived species with larger body sizes, dynamic approaches performed similarly to static models. We advocate for increased use of fine-scale, long-term data in SDMs to boost the performance and reliability of future predictions under global change.