Different groups' grazing technique preferences in the Miombo woodlands of Eastern Tanzania.

Grazing in Miombo woodlands is essential for the livelihoods of the pastoral and agropastoral communities that live adjacent to these fragile ecosystems. However, Miombo woodlands offer not only fodder for livestock but also fertile land for crop farming; hence, they are equally important to the farmers residing in these areas. Due to the importance of the Miombo woodlands for the livelihoods of several groups, the consumption of and competition for Miombo resources has increased over time and now threatens the overall health of the ecosystem. This study aimed to identify the grazing techniques practiced by different livelihood groups in Miombo woodlands; their preferences for different practices, as well as the factors that influence these preferences, so as to understand how sustainable grazing can be achieved for better ecosystem health. The study was conducted in Handeni, Kilombero, and Kilosa districts and covered pastoralists, agropastoralists, and farmers. We carried out focus group discussions, key informant interviews, and a survey of 246 respondents. Un-patterned rotational grazing was the most preferred grazing technique by all three groups, and the only technique the three groups shared a preference for. All the groups took a neutral stance in relation to continuous grazing. Their preferences for other grazing techniques differ. The study highlights the need to raise awareness amongst pastoralists about land ownership and management and recommends enhancing land property rights for all groups in order to harmonize livestock keeping and other land uses for achieving sustainable grazing and overall ecosystem health in the fragile Miombo woodlands of Tanzania.

Use of Multi-Date and Multi-Spectral UAS Imagery to Classify Dominant Tree Species in the Wet Miombo Woodlands of Zambia.

Accurate maps of tree species distributions are necessary for the sustainable management of forests with desired ecological functions. However, image classification methods to produce species distribution maps for supporting sustainable forest management are still lacking in the Miombo woodland ecoregion. This study used multi-date multispectral Unmanned Aerial Systems (UAS) imagery collected at key phenological stages (leaf maturity, transition to senescence, and leaf flushing) to classify five dominant canopy species of the wet Miombo woodlands in the Copperbelt Province of Zambia. Object-based image analysis (OBIA) with a random forest algorithm was used on single date, multi-date, and multi-feature UAS imagery for classifying the dominant canopy tree species of the wet Miombo woodlands. It was found that classification accuracy varies both with dates and features used. For example, the August image yielded the best single date overall accuracy (OA, 80.12%, 0.68 kappa), compared to October (73.25% OA, 0.59 kappa) and May (76.64% OA, 0.63 kappa). The use of a three-date image combination improved the classification accuracy to 84.25% OA and 0.72 kappa. After adding spectral indices to multi-date image combination, the accuracy was further improved to 87.07% and 0.83 kappa. The results highlight the potential of using multispectral UAS imagery and phenology in mapping individual tree species in the Miombo ecoregion. It also provides guidance for future studies using multispectral UAS for sustainable management of Miombo tree species.

Contrasted histories of organelle and nuclear genomes underlying physiological diversification in a grass species.

C4 photosynthesis evolved multiple times independently in angiosperms, but most origins are relatively old so that the early events linked to photosynthetic diversification are blurred. The grass Alloteropsis semialata is an exception, as this species encompasses C4 and non-C4 populations. Using phylogenomics and population genomics, we infer the history of dispersal and secondary gene flow before, during and after photosynthetic divergence in A. semialata. We further analyse the genome composition of individuals with varied ploidy levels to establish the origins of polyploids in this species. Detailed organelle phylogenies indicate limited seed dispersal within the mountainous region of origin and the emergence of a C4 lineage after dispersal to warmer areas of lower elevation. Nuclear genome analyses highlight repeated secondary gene flow. In particular, the nuclear genome associated with the C4 phenotype was swept into a distantly related maternal lineage probably via unidirectional pollen flow. Multiple intraspecific allopolyploidy events mediated additional secondary genetic exchanges between photosynthetic types. Overall, our results show that limited dispersal and isolation allowed lineage divergence, with photosynthetic innovation happening after migration to new environments, and pollen-mediated gene flow led to the rapid spread of the derived C4 physiology away from its region of origin.

Dry and wet miombo woodlands of south-central Africa respond differently to climate change.

It is important to understand how species distributions will shift under climate change. While much focus has been on species tracking temperature changes in the northern hemisphere, changing precipitation patterns in tropical regions have received less attention. The aim of the study was to estimate the current distribution of wet and dry miombo woodlands of sub-Saharan Africa and to predict their distributions under different climate change scenarios. A maximum entropy method (Maxent) was used to estimate the distributions and for projections. Occurrence records of dominant tree species in each woodland were used for modeling, together with altitude, soil characteristics, and climate variables as the environmental variables. Modeling was done under all four representative concentration pathways (RCPs) and three general circulation models. Three dominant tree species were used in models of dry miombo while seven were used for wet miombo. Models estimated dry miombo to cover almost the entire known distribution of miombo woodlands while wet miombo were estimated to predominate in parts of Angola, southern Democratic Republic of Congo, Malawi, Tanzania, Zambia, and Zimbabwe. Future climate scenarios predict a drier climate in sub-Saharan Africa, and as a result, the range of dry miombo will expand. Dry miombo were predicted to expand by up to 17.3% in 2050 and 22.7% in 2070. In contrast, wet miombo were predicted to contract by up to - 28.6% in 2050 and - 41.6% in 2070. A warming climate is conducive for the proliferation of dry miombo tree species but unfavorable for wet miombo tree species.

Human and natural controls of the variation in aboveground tree biomass in African dry tropical forests.

Understanding the anthropogenic and natural controls that affect the patterns, distribution, and dynamics of terrestrial carbon is crucial to meeting climate change mitigation objectives. We assessed the human and natural controls over aboveground tree biomass density in African dry tropical forests, using Zambia's first nationwide forest inventory. We identified predictors that best explain the variation in biomass density, contrasted anthropogenic and natural sites at different spatial scales, and compared sites with different stand structure characteristics and species composition. In addition, we evaluated the effects of different management and conservation practices on biomass density. Variation in biomass density was mostly determined by biotic processes, linked with both species richness and dominance (evenness), and to a lesser extent, by land use, environmental controls, and spatial structure. Biomass density was negatively associated with tree species evenness and positively associated with species richness for both natural and human-modified sites. Human influence variables (including distance to roads, distance to town, fire occurrence, and the population on site) did not explain substantial variation in biomass density in comparison to biodiversity variables. The relationship of human activities to biomass density in managed sites appears to be mediated by effects on species diversity and stand structure characteristics, with lower values in human-modified sites for all metrics tested. Small contrasts in carbon density between human-modified and natural forest sites signal the potential to maintain carbon in the landscape inside but also outside forestlands in this region. Biodiversity is positively related to biomass density in both human and natural sites, demonstrating potential synergies between biodiversity conservation and climate change mitigation. This is the first evidence of positive outcomes of protected areas and participatory forest management on carbon storage at national scale in Zambia. This research shows that understanding controls over biomass density can provide policy relevant inputs for carbon management and on ecological processes affecting carbon storage.

Combining airborne laser scanning and Landsat data for statistical modeling of soil carbon and tree biomass in Tanzanian Miombo woodlands.

BACKGROUND: Soil carbon and biomass depletion can be used to identify and quantify degraded soils, and by using remote sensing, there is potential to map soil conditions over large areas. Landsat 8 Operational Land Imager satellite data and airborne laser scanning data were evaluated separately and in combination for modeling soil organic carbon, above ground tree biomass and below ground tree biomass. The test site is situated in the Liwale district in southeastern Tanzania and is dominated by Miombo woodlands. Tree data from 15 m radius field-surveyed plots and samples of soil carbon down to a depth of 30 cm were used as reference data for tree biomass and soil carbon estimations. RESULTS: Cross-validated plot level error (RMSE) for predicting soil organic carbon was 28% using only Landsat 8, 26% using laser only, and 23% for the combination of the two. The plot level error for above ground tree biomass was 66% when using only Landsat 8, 50% for laser and 49% for the combination of Landsat 8 and laser data. Results for below ground tree biomass were similar to above ground biomass. Additionally it was found that an early dry season satellite image was preferable for modelling biomass while images from later in the dry season were better for modelling soil carbon. CONCLUSION: The results show that laser data is superior to Landsat 8 when predicting both soil carbon and biomass above and below ground in landscapes dominated by Miombo woodlands. Furthermore, the combination of laser data and Landsat data were marginally better than using laser data only.

Mapping and estimating the total living biomass and carbon in low-biomass woodlands using Landsat 8 CDR data.

BACKGROUND: A functional forest carbon measuring, reporting and verification (MRV) system to support climate change mitigation policies, such as REDD+, requires estimates of forest biomass carbon, as an input to estimate emissions. A combination of field inventory and remote sensing is expected to provide those data. By linking Landsat 8 and forest inventory data, we (1) developed linear mixed effects models for total living biomass (TLB) estimation as a function of spectral variables, (2) developed a 30 m resolution map of the total living carbon (TLC), and (3) estimated the total TLB stock of the study area. Inventory data consisted of tree measurements from 500 plots in 63 clusters in a 15,700 km2 study area, in miombo woodlands of Tanzania. The Landsat 8 data comprised two climate data record images covering the inventory area. RESULTS: We found a linear relationship between TLB and Landsat 8 derived spectral variables, and there was no clear evidence of spectral data saturation at higher biomass values. The root-mean-square error of the values predicted by the linear model linking the TLB and the normalized difference vegetation index (NDVI) is equal to 44 t/ha (49 % of the mean value). The estimated TLB for the study area was 140 Mt, with a mean TLB density of 81 t/ha, and a 95 % confidence interval of 74-88 t/ha. We mapped the distribution of TLC of the study area using the TLB model, where TLC was estimated at 47 % of TLB. CONCLUSION: The low biomass in the miombo woodlands, and the absence of a spectral data saturation problem suggested that Landsat 8 derived NDVI is suitable auxiliary information for carbon monitoring in the context of REDD+, for low-biomass, open-canopy woodlands.