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Climate change is expected to have detrimental consequences on fragile ecosystems, threatening biodiversity as well as food security of millions of people. Trees are likely to play a central role in mitigating these impacts. The microclimatic conditions below tree canopies usually differ substantially from the ambient macroclimate, as vegetation can buffer temperature changes and variability. Trees cool down their surroundings through several biophysical mechanisms, and the cooling benefits occur also with trees outside forest. The aim of this study was to examine the effect of canopy cover on microclimate in an intensively modified Afromontane landscape in Taita Taveta, Kenya. We studied temperatures recorded by 19 microclimate sensors under different canopy covers, and land surface temperature (LST) estimated by Landsat 8 thermal infrared sensor. We combined the temperature records with high-resolution airborne laser scanning data to untangle the combined effects of topography and canopy cover on microclimate. We developed four multivariate regression models to study the joint impacts of topography and canopy cover on LST. The results showed a negative linear relationship between canopy cover percentage and daytime mean (R² = 0.65) and maximum (R² = 0.75) temperatures. Any increase in canopy cover contributed to reducing temperatures. The average difference between 0% and 100% canopy cover sites was 5.7 °C in mean temperatures and 10.2 °C in maximum temperatures. Canopy cover reduced LST on average by 0.05 °C/%CC. The influence of canopy cover on microclimate was shown to vary strongly with elevation and ambient temperatures. These results demonstrate that trees have substantial effect on microclimate, but the effect is dependent on macroclimatic conditions, highlighting the importance of maintaining tree cover particularly in warmer conditions. Hence, we demonstrate that trees outside forests can increase climate change resilience in fragmented landscapes, having strong potential for regulating regional and local temperatures. [ABSTRACT FROM AUTHOR]

Though there is an increase in popularity of predictive modelling for assessing the geographical distribution of species, there is still a clear gap on explaining geospatial methods to derive the presence/absence of species in terms of geospatial extent besides the ambiguity of robust models. In this paper, we evaluate four major species distribution modelling methods: Artificial Neural Network (ANN), Support Vector Machines (SVM), Maximum Entropy (MaxEnt) and Generalized Linear Model (GLM) with pseudo absence and background absence data. To investigate the efficacy of these models, we present a case study using Coffea arabica L. species in Ethiopia as there was no species distribution modelling that has been done at a local scale especially in the coffee growing areas. We made predictions on 75% subsets and validation on 25% of the 112 presence of the species records that were collected from field observation and 0.5 m spatial resolution of true colour aerial photographs. Twelve biophysical explanatory variables; climatic, remote sensing based and landscape variables were employed in modelling. The results show that MaxEnt with pseudo absence data and SVM with background absence have highest area of understory coffee presence prediction with 12.2% and 23.1% area coverage of indigenous forest, respectively. The result from the model performance test using True Positive Rate (TPR) shows that GLM and SVM with pseudo absence data performed highest (TPR = 0.821). MaxEnt and SVM were the robust modelling methods (TPR = 0.964) using background absence data. [ABSTRACT FROM AUTHOR]

Forest canopy structural complexity (CSC) plays a crucial role in shaping forest ecosystem productivity and stability, but the precise nature of their relationships remains controversial. Here, we mapped the global distribution of forest CSC and revealed the factors influencing its distribution using worldwide light detection and ranging data. We find that forest CSC predominantly demonstrates significant positive relationships with forest ecosystem productivity and stability globally, although substantial variations exist among forest ecoregions. The effects of forest CSC on productivity and stability are the balanced results of biodiversity and resource availability, providing valuable insights for comprehending forest ecosystem functions. Managed forests are found to have lower CSC but more potent enhancing effects of forest CSC on ecosystem productivity and stability than intact forests, highlighting the urgent need to integrate forest CSC into the development of forest management plans for effective climate change mitigation.. [ABSTRACT FROM AUTHOR]

The article offers information on Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. It mentions the magnitude of carbon losses at these forest edges is still poorly quantified at large scales due to the scarcity of quantitative datasets for tropical forests, Efforts to accurately incorporate this source to regional and global carbon budgets.