Assessing Tree Coverage and the Direct and Mediation Effect of Tree Diversity on Carbon Storage through Stand Structure in Homegardens of Southwestern Bangladesh

Dealing with two major challenges, climate change mitigation and biodiversity loss, under the same management program, is more noteworthy than addressing these two separately. Homegardens, a sustainable agroforestry system and a home of diverse species, can be a possible choice to address these two issues. In this study, we assessed tree coverage, and the direct and indirect effects of tree diversity on carbon storage in different carbon pools through stand structure in homegardens of southwestern Bangladesh, using Sentinel 2 and field inventory data from 40 homesteads in eight villages. An unsupervised classification method was followed to assess homegardens’ tree coverage. We found a high tree coverage (24.34% of total area of Dighalia) in homesteads, with a high overall accuracy of 96.52%. The biomass and soil organic carbon (p < 0.05) varied significantly among the eight villages, while total carbon stock did not vary significantly (p > 0.05). Shannon diversity had both direct and indirect effects on biomass carbon, upper layer soil organic carbon and total carbon storage, while basal area mediated the indirect effect. Both basal area and tree height had positive effects on biomass carbon and total carbon storage, with basal area having the strongest effect. These findings suggest that we must maintain higher diversity and tree height in order to maximize and sustain carbon storage, where tree diversity increases stand basal area and improves total carbon storage (including soil organic) in homegardens. Therefore, privately managed homegardens could be a potential nature-based solution for biodiversity conservation and climate change mitigation in Bangladesh.

Assessment of Coffea Shade Value through Comparison of Mountainous Area with Farm Land Coverage, in Arsi Gololcha District, Oromia, Ethiopia

Coffea cultivation with shade tree is used for improving soil health, increasing coffea production, sustaining agro ecology. The study was attended in two kebele, on 36 farmers’ fields, at Gololcha district of East Arsi zone. The study was intended to assess the influence of coffea shade trees on farm lands versus mountainous area. Household interviews were used to get imperative separately, i.e. from old farmers, middle age farmers and young farmers. Significant difference value was observed between farm land and mountainous area coverage. Based on this respondents’ idea, before 25-30 years; the ‘condition of tree coverage at mountainous’ area in Arsi Gololcha district was ‘medium condition’ but not normal that means as deforestation of mountainous area have been starting before 30 years’ time; while the condition of tree coverage at farmland area also has been starting before 30 years’ time. The third respondents’ idea was interpreted with the real situation of the district, that it gave us a constructive inspiration on the role of coffea shade tree to enable the farm land to be taken as regular natural forest. The existing coffea shade trees are Cordia africana followed by Erythrina abyssinica and Acacia senegal. Farmers accounted 95% of coffea shade users and 4.6% without shade users. The respondents said that even if the rainfall intensity is increasing at farmland rather than mountainous area occasionally due to shade tree effect. On the contrary side, mountainous area exposed to deforestation since the farmers have been shifting to hilly side for their livelihood dependency.

Tree - Open Grassland Mosaics Drive the Herbaceous Structure and Diversity in Mediterranean Holm Oak Meadows

Background: Mediterranean holm oak meadows are semi‑natural savannah‑like agroecosystems that result from traditional silvo‑pastoral practices, which shaped these systems into a mosaic of trees and open grassland. However, traditional silvo-pastoral uses are declining with the implications that this may have on the herbaceous layer, a very biodiverse and valuable resource of these systems. Here, we aim at assessing the influence of the tree – open grassland mosaic on the structure, diversity, and composition of the herbaceous layer. Specifically, assessing the canopy effect (a) under representative Iberian canopy types, considering traditional Quercus species stands and Pinus pinea plantations at different locations; and (b) along seasonality. Results: The different components of the herbaceous layer performed differential responses to the presence/absence of tree canopies, as for instance shows the dominance of grasses under the canopy, while legumes and forbs were favoured in the open grassland. Also, there was a certain a reduction in the species richness in P. pinea dominated plots compared to plots dominated by Quercus species. There was a reduction of the aboveground biomass under the canopy at the more environmentally constrained location. Such canopy effects were generally more pronounced in spring that in autumn.Conclusion: It is highly advisable preserve the tree – open grassland mosaic and traditional Quercus species stands to maximize and preserve plant specific and functional diversity. The the optimum tree coverage might be dependent, not only on the primary ecosystem service (i. e. forage provision), but also on local conditions.

The effects of varying drought-heat signatures on terrestrial carbon dynamics and vegetation composition

The frequency and severity of droughts and heat waves are projected to increase under global warming. However, the differential impacts of climate extremes on the terrestrial biosphere and anthropogenic CO2 sink remain poorly understood. In this study, we analyse the effects of six hypothetical climate scenarios with differing drought-heat signatures, sampled from a long stationary climate model simulation, on vegetation distribution and land carbon dynamics, as modelled by a dynamic global vegetation model (LPX-Bern v1.4). The six forcing scenarios consist of a Control scenario representing a natural climate, a Noextremes scenario featuring few droughts and heatwaves, a Nocompound scenario which allows univariate hot or dry extremes but no co-occurring extremes, a Hot scenario with frequent heatwaves, a Dry scenario with frequent droughts, and a Hotdry scenario featuring frequent concurrent hot and dry extremes. We find that a climate with no extreme events increases tree coverage by up to 10 % compared to the Control and also increases ecosystem productivity as well as the terrestrial carbon pools. A climate with many heatwaves leads to an overall increase in tree coverage primarily in higher latitudes, while the ecosystem productivity remains similar to the Control. In the Dry and even more so in the Hotdry scenario, tree cover and ecosystem productivity are reduced by up to −4 % compared to the Control. Depending on the vegetation type, the effects from the Hotdry scenario are stronger than the effects from the Hot and Dry scenario combined, illustrating the importance of correctly simulating compound extremes for future impact assessment. Overall, our study illustrates how factorial model experiments can be employed to disentangle the effects from single and compound extremes.

Areas for the connectivity of Chaco peccary (Catagonus wagneri) populations at the southern limit of their distribution in the Argentine Chaco / Áreas para a conectividade de populações de pecari do Chaco (Catagonus wagneri) no limite sul de sua distribuição no Chaco Argentino

The area of geographical distribution of mammal populations in the Argentine Chaco ecoregion is being increasingly reduced and this is mainly due to the progressive destruction of habitats. In this context, several species have been affected, among which is the endemic Catagonus wagneri (Tayassuidae), currently classified as "endangered" and with a trend of population decline. In this work, the predictions estimated by three algorithms were compared to establish the potential geographic distribution of this species at the southern limit of its natural distribution. Priority locations for landscape connectivity were identified by comparing intrinsic variations in the PC index based on data classification methods. With the use of foot transects, trap cameras and surveys with the local population, the presence of chacoan peccary was recorded on 25 occasions. From the GLM, Random Forest and Maxent algorithms (mean AUC 0.74), a reference model was obtained. Using it as an input and the PC index, the variation in the importance of the connectivity surfaces of the landscape was evaluated using three classification methods: quantile, equal interval and natural breaks. The consensus model (SDM) occupies 55,674 km2 of Argentina, representing 10% of the Chaco Seco ecoregion. The distribution occupies not only forest ecosystems, but also environments with less tree coverage. Coefficients of variation of 170% were recorded between the classification methods for the number of patches of classes 9 and 10 of the priority habitat for landscape connectivity. The SDM shows a fragmented distribution in line with the Chaco's land use change process.The results suggest a great variability of the PC index depending on the method of classifying data in class intervals, an aspect that was not discussed in previous studies.

Pronounced loss of Amazon rainforest resilience since the early 2000s

The resilience of the Amazon rainforest to climate and land-use change is of critical importance for biodiversity, regional climate, and the global carbon cycle. Some models project future climate-driven Amazon rainforest dieback and transition to savanna1. Deforestation and climate change, via increasing dry-season length2,3 and drought frequency – with three 1-in-100-year droughts since 20054-6 – may already have pushed the Amazon close to a critical threshold of rainforest dieback7,8. However, others argue that CO2 fertilization should make the forest more resilient9,10. Here we quantify Amazon resilience by applying established indicators11 to remotely-sensed vegetation data with focus on vegetation optical depth (1991-2016), which correlates well with broadleaf tree coverage. We find that the Amazon rainforest has been losing resilience since 2003, consistent with the approach to a critical transition. Resilience is being lost faster in regions with less rainfall, and in parts of the rainforest that are closer to human activity. Given observed increases in dry-season length2,3 and drought frequency4-6, and expanding areas of land use change, loss of resilience is likely to continue. We provide direct empirical evidence that the Amazon rainforest is losing stability, risking dieback with profound implications for biodiversity, carbon storage and climate change at a global scale.

Modelling children’s independent territorial range by discretionary and nondiscretionary trips

The decline of children’s independent mobility (CIM) is now a global concern. This study aims to identify the determinants of the territorial range (TR) of CIM, i.e., the geographical distance between home and places where children are allowed to wander. TR for both discretionary and nondiscretionary trips is studied based on data collected through a questionnaire survey, travel diary, and mapping of travel routes. The study sample was comprised of 151 children 9-14 years of age from Dhaka, Bangladesh. Built environment (BE) data were collected/derived through walkability audits of children’s walking routes and spatial analyses. Children’s TR was regressed by BE, socio-demographics, and perceptual factors. Three multiple regression models were estimated: overall TR, discretionary TR, and nondiscretionary TR. Results showed that children had a longer TR for nondiscretionary trips (664.14 m) compared to discretionary trips (397.9 m). Discretionary TR was largely explained by angular step-depth, street connectivity and the condition of the walking environment of the taken routes. In contrast, angular step-depth, the presence of commercial and retail land uses and the condition of the walking environment were found to be significant predictors of nondiscretionary TR. Children’s perception of social and physical dangers and their satisfaction with tree coverage in the neighborhood also influenced their TR. The findings can inform measures to be taken to expand TR in the urban environment.

Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN)

Urban trees provide social, economic, environmental and ecosystem services benefits that improve the liveability of cities and contribute to individual and community wellbeing. There is thus a need for effective mapping, monitoring and maintenance of urban trees. Remote sensing technologies can effectively map and monitor urban tree coverage and changes over time as an efficient and low-cost alternative to field-based measurements, which are time consuming and costly. Automatic extraction of urban land cover features with high accuracy is a challenging task, and it demands object based artificial intelligence workflows for efficiency and thematic accuracy. The aim of this research is to effectively map urban tree cover changes and model the relationship of such changes with socioeconomic variables. The object-based convolutional neural network (CNN) method is illustrated by mapping urban tree cover changes between 2005 and 2015/16 using satellite, Google Earth imageries and Light Detection and Ranging (LiDAR) datasets. The training sample for CNN model was generated by Object Based Image Analysis (OBIA) using thresholds in a Canopy Height Model (CHM) and the Normalised Difference Vegetation Index (NDVI). The tree heatmap produced from the CNN model was further refined using OBIA. Tree cover loss, gain and persistence was extracted, and multiple regression analysis was applied to model the relationship with socioeconomic variables. The overall accuracy and kappa coefficient of tree cover extraction was 96% and 0.77 for 2005 images and 98% and 0.93 for 2015/16 images, indicating that the object-based CNN technique can be effectively implemented for urban tree coverage mapping and monitoring. There was a decline in tree coverage in all suburbs. Mean parcel size and median household income were significantly related to tree cover loss (R2 = 58.5%). Tree cover gain and persistence had positive relationship with tertiary education, parcel size and ownership change (gain: R2 = 67.8% and persistence: R2 = 75.3%). The research findings demonstrated that remote sensing data with intelligent processing can contribute to the development of policy input for management of tree coverage in cities.