Heterogeneous Savanna Research Articles

Balanced Functional Herbivore Composition Stabilizes Tree-Grass Coexistence and Productivity in a Simulated Savanna Rangeland Ecosystem

Savanna ecosystems provide resources supporting the livelihoods of millions of people globally. Ongoing exploitation through high livestock densities, among other factors, however, threatens these ecosystems. Shrub encroachment, a consequence of this exploitation, causes severe declines in fodder biomass for cattle. For example, in Namibia, this leads to losses of land productivity of up to 100%. Consequently, we must rethink land-use options to ensure sustainable utilization of savanna resources. In southern Africa, many landowners and custodians have switched to or included wildlife in their management systems, making it an excellent model region to study how changing herbivory affects ecosystems. The diversity of indigenous wildlife species is expected to promote efficient herbaceous layer utilization with browsing herbivores counteracting nondesirable woody plant encroachment. Nevertheless, surprisingly little research has been conducted on the ecological consequences of altering grazer-to-browser ratios.We developed a large-scale spatially explicit dynamic vegetation model including key hydrological and ecological processes, fire, and an herbivore algorithm that accounts for aggregated animal herds in a heterogeneous savanna landscape. We find that different ratios of wild herbivore feeding types greatly influence vegetation composition and dynamics. By dynamically computing plant biomass as a resource, our model estimates the occurrence of fodder biomass shortages, providing a valuable tool for land users. The model also reports the number of patches with low to medium tree functional cover (10–25%), indicating structural diversity with its implications for species richness, and is thus essential for nature conservation. We find that a herbivore species composition of approximately 40% browsers and 60% grazers is beneficial for plant structural and species diversity. Theoretically, therefore, the browse-graze ratio achieved through wildlife-based land-use can lead to more sustainability and resilience. The interplay between diverse herbivory, vegetation, rain, and fire complicates but also stabilizes the savanna rangeland ecosystem and may contribute to its long-term persistence.

Effects of long‐term fixed fire regimes on African savanna vegetation biomass, vertical structure and tree stem density

Abstract Fire plays an integral role in shaping the vegetation structure of savanna ecosystems. However, the effects of fire regime characteristics, such as frequency and season of burn, on savanna vegetation structure, biomass and tree abundance across landscape types are largely unknown. We used high‐resolution airborne light detection and ranging (LiDAR) to investigate the long‐term effects of fire manipulation on savanna vegetation in Kruger National Park, South Africa. We analysed the effects of fire exclusion and experimental burns every 1, 2, 3, 4 and 6 years and during different seasons on aboveground biomass (AGB), tree stem densities and vegetation vertical height profiles across a rainfall gradient and on contrasting geologies. Across savanna types, and especially in drier savannas, fire season was more influential for constraining AGB than fire frequency. Plots experiencing fires during the late‐ and mid‐dry season had 44.50% and 43.60%, respectively, lower AGB relative to unburnt plots than wet‐season fires. However, in mesic savannas, fire frequency interacted with fire season to influence AGB: plots subjected to high frequency, dry‐season fires had 55.35% lower AGB than unburnt plots, whereas plots burnt in the wet season at lower frequencies had lower AGB (24.40% lower than unburnt plots) than plots subjected to high frequency, wet‐season fires (13.74% lower AGB than unburnt plots). Fire regimes had variable effects on tree densities, and effects varied with the savanna type. Woody vertical vegetation profiles showed the largest differences in response to dry‐season fires, with the greatest divergence in vegetation height classes <5 m. Synthesis and applications. Understanding the influence of fire regimes on vegetation structure has important implications for the management of savanna heterogeneity and for predicting trajectories of change in savanna vegetation as fire regimes vary with climate change. We show that the magnitude of the effect of fire on woody vegetation structure varies with savanna context. Our results suggest that heterogeneous vegetation structure can be achieved by applying fires in the dry season in mesic savannas, whereas in dry savannas, variation in fire regimes is less consequential for constraining biomass accumulation and altering vegetation structure.

Spatiotemporal trends in floral visitation and interaction networks reveal shifting niches for bats in a Neotropical savanna.

Flower-vising bats are important components of tropical pollinator communities, yet little is known about the structure of their pollination networks and how resource availability through time (seasons) and space (habitat heterogeneity) affects the extent to which bats interact with plants within a community-wide context. This information is key for the conservation of threatened nectarivore species, such as the Cerrado-endemic Lonchophylla dekeyseri, for which data on its specialization on floral-resources is scarce. Within a seasonal and heterogeneous savanna in the central Brazilian Cerrado, we performed a year-round assessment of an inclusive assemblage of flower-visiting bats (both nectarivores and other guilds that can also feed on nectar) within a savanna-edge-forest gradient, the phenological trends and spatial distribution of bat and their resource plants, and the resultant temporal and spatial interaction networks between bats and plants in order to associate network structure to resource availability. Clear spatiotemporal trends emerged in the community. Nectarivores dominated the flower-visiting niche outside forests and were prolific floral visitors, resulting in networks with lower specialization and modularity. These bats diverged into savanna foragers active during the wet season and the wet-dry transition, and edge foragers active mostly during the dry season. The latter group encompassed L. dekeyseri, which visited mostly Bauhinia species. Frugivores took over as main floral visitors within forests, as well as during peak dry season, when fewer fruits were available, resulting in more specialized and modular networks. Our work shows that the turnover of floral resources across seasons and vegetation types has a defining role in bat-plant interactions and relates to network structure, as bat trophic guilds interact with plants in distinct habitats and times of the year. Frugivores dominate the flower-visiting niche in certain temporal and spatial subsets of the network, which calls for the inclusion of this guild in future studies. Moreover, the high visitation to Bauhinia species by L. dekeyseri during the dry season might reduce competition with other nectarivores and is relevant to the management of the species, although more data is needed on its resource consumption on a larger time frame and across its geographic range.

Validation of LAI, chlorophyll and FVC biophysical estimates from sentinel-2 level 2 prototype processor over a heterogeneous savanna and grassland environment in South Africa

The Sentinel-2 Level 2 Prototype Processor (SL2P) allows the generation of biophysical estimates at high spatiotemporal resolution from Sentinel-2 imagery and could be a solution for generating products in natural environments. This study validated the SL2P estimates of leaf area index (LAI), fractional vegetation cover (FVC) and canopy chlorophyll content (CCC) over the savanna and grassland environments using field measurements. The performance of the SL2P estimates in Marakele and Golden Gate Highlands National Parks were comparatively poor and linearly biased coupled with moderate-to-high errors. The SL2P estimates in the two study sites had low accuracy with relative root mean squared error’s in the range 61.63% to 85.26% and possible systematic underestimations with pBias's ranging from 32.17% to 63.16%. These findings gave insight about the performance of the SL2P estimates over the considered heterogenous environments, and suggest the need for extensive validation and re-calibration of the system using long-term field measurements.

Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform

Widespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985–2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr−1), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other land uses but is now in some level of secondary NV. Our results were fundamental in indicating areas with higher rates of change in a long time series in the Brazilian Cerrado and to highlight the challenges of mapping distinct NV types in a highly seasonal and heterogeneous savanna biome.

Auxiliary datasets improve accuracy of object-based land use/land cover classification in heterogeneous savanna landscapes

Classifying land use/land cover (LULC) with sufficient accuracy in heterogeneous landscapes is challenging using only satellite imagery. To improve classification accuracy inclusion of features from auxiliary geospatial datasets in classification models is applied since 1980s. However, the method is mostly limited to pixel-based classifications, and the coverage, accuracy and resolution of free and open-access auxiliary datasets have been poor until recent years.We evaluated how recent global coverage open-access geospatial datasets improve object-based LULC classification accuracy compared to using only spectral and texture features from satellite images. We applied feature sets topography, population, soil, canopy cover, distance to watercourses and spectral-temporal metrics from Landsat-8 time series on the southern foothills and savanna of Mt. Kilimanjaro, Tanzania, where the landscape is characterized by heterogeneous and fragmented mosaic of disturbed savanna vegetation, croplands, and settlements.The classification was based on image objects (groups of spectrally similar pixels) derived from segmentation of four Formosat-2 scenes with 8 m spatial resolution using 1370 ground reference points for training, validation, and for defining 17 LULC classes. We built six Random Forest classification models with different sets of object features in each. The baseline model having only spectral and texture features was compared with five other models supplemented with auxiliary features.Inclusion of auxiliary features significantly improved classification overall accuracy (OA). The baseline model gave a median OA of 60.7%, but auxiliary features in other models increased median OA between 6.1 and 16.5 percentage points. The best OA was achieved with a model including all features of which elevation was the most important auxiliary feature followed by Enhanced Vegetation Index temporal range and slope degree.Applying object-based classification to geospatial information on topography, soil, settlement patterns and vegetation phenology, the discriminatory potential of challenging LULC classes can be significantly improved. We demonstrated this for the first time, and the technique shows good potential for improving LULC mapping across a multitude of fragmented landscapes worldwide.

The effect of experimental fires on soil hydrology and nutrients in an African savanna

Savannas make up about 20% of the global land-surface and are dependent on fires to maintain a balanced ecosystem. Fires in other fire-driven landscapes, particularly wildfires, were found to have negative effects on various soil properties. However, there is a lack of studies confirming the effect of fires on soil hydrology in African savanna soils. A long-term fire experiment in a South African savanna provided an opportunity to investigate the effect of different prescribed fire frequencies on soil properties in situ across coarse-grained granitic and fine-textured basalt-derived soils. Soil properties were compared between soils exposed to annual fires, fires every 2–4 years and where fires have been excluded for approximately 60 years. Across all three fire treatments, unsaturated hydraulic conductivity (Kunsat) was measured using a Tension Disc Infiltrometer to infer infiltration rates, saturated hydraulic conductivity (Ksat) measured with a Guelph Permeameter, soil water potential calculated using a Decagon WP4-T Dewpoint Potentiometer to infer soil water retention and soil total C and N measured using a LECO CNS TruMac Series Analyser. Our study found that Kunsat is not affected by frequent annual fires which have infiltration rates similar to soils where fires have been excluded for nearly 6 decades. However, recently burnt granitic soils, i.e. three months prior, have significantly slower Kunsat which were as low as <1 mm hr−1 compared to a mean Kunsat of 30 mm hr−1 on annually burnt soils, alluding to short term fire impacts on soil infiltration. Hence, we believe that time following a fire plays a greater role on Kunsat than fire frequency. Fires did not affect Ksat within the initial 2–5 cm of the soil surface. In general, the granitic soils had faster Kunsat and Ksat than the basaltic soils. Soil water potential, total C and N was significantly greater in the fire exclusion sites over both parent materials. Soil water and nutrient availability is critical in a post-fire environment to facilitate vegetation recovery in African savannas. These systems are resilient to fires which do not have long-term negative impacts on soil hydrology and nutrients, but instead increases the spatio-temporal variation in soil properties necessary in maintaining savanna heterogeneity.

Using a coupled dynamic factor - random forest analysis (DFRFA) to reveal drivers of spatiotemporal heterogeneity in the semi-arid regions of southern Africa.

Understanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude, and spatial distribution of the key environmental and socioeconomic factors driving vegetation change in a southern African savanna. This research was conducted across the Kwando, Okavango and Zambezi catchments of southern Africa (Angola, Namibia, Botswana and Zambia) and explored vegetation cover change across the region from 2001–2010. A novel coupled analysis was applied to model the dynamic biophysical factors then to determine the discrete / social drivers of spatial heterogeneity on vegetation. Previous research applied Dynamic Factor Analysis (DFA), a multivariate times series dimension reduction technique, to ten years of monthly remotely sensed vegetation data (MODIS-derived normalized difference vegetation index, NDVI), and a suite of time-series (monthly) environmental covariates: precipitation, mean, minimum and maximum air temperature, soil moisture, relative humidity, fire and potential evapotranspiration. This initial research was performed at a regional scale to develop meso-scale models explaining mean regional NDVI patterns. The regional DFA predictions were compared to the fine-scale MODIS time series using Kendall’s Tau and Sen’s Slope to identify pixels where the DFA model we had developed, under or over predicted NDVI. Once identified, a Random Forest (RF) analysis using a series of static social and physical variables was applied to explain these remaining areas of under- and over- prediction to fully explore the drivers of heterogeneity in this savanna system. The RF analysis revealed the importance of protected areas, elevation, soil type, locations of higher population, roads, and settlements, in explaining fine scale differences in vegetation biomass. While the previously applied DFA generated a model of environmental variables driving NDVI, the RF work developed here highlighted human influences dominating that signal. The combined DFRFA model approach explains almost 90% of the variance in NDVI across this landscape from 2001–2010. Our methodology presents a unique coupling of dynamic and static factor analyses, yielding novel insights into savanna heterogeneity, and providing a tool of great potential for researchers and managers alike.

Termitaria vs. mistletoe: Effects on soil properties and plant structure in a semi-arid savanna

Termite mounds by creating patches of increased resource availability (e.g. water and nutrients) are a major source of spatial heterogeneity in savannas. Likewise, mistletoes via input of nutrient-rich litter alter nutrient and water availability increasing environmental heterogeneity in semi-arid savanna. Despite this recognition, the influence of termitaria and mistletoe on soil properties and plant community have not been investigated together. We established eight 100 m2 plots each on termitaria, under mistletoe-infected trees and in the surrounding savanna and examined the soil properties and the structure of Securinega virosa (Euphorbiaceae) and Euclea divinorum (Ebenaceae) in semi-arid savanna, southwest Zimbabwe. Soil properties significantly differed among the sampling sites (p = 0.001) with soils of increasing clay, soil moisture, pH and phosphorus, calcium and ammonium concentrations occurring on termite mounds. Soils under mistletoe-infected trees were associated with silt, organic matter, sodium, potassium, magnesium and nitrate and the surrounding savanna was associated with soils of increasing sand content. Plant structure also differed significantly between sites with greater basal area of both S. virosa and E. divinorum on termitaria relative to mistletoe-infected trees and the surrounding savanna. However, the stem density of S. virosa was greater under mistletoe-infected trees than on termitaria and in the surrounding savanna. Plant structural variables of individuals of the same species were affected by different soil properties across treatments. The major patterns showed that plant structure was influenced positively by soil moisture and nitrate and negatively by phosphorus on termitaria; positively by clay, soil moisture and ammonium and negatively by potassium under mistletoe-infected trees; and by phosphorus and calcium in the surrounding savanna. These findings show that soil properties, plant structure and their relationships differ between termitaria, mistletoe-infected trees and surrounding savanna, and these differences are suggested to increase heterogeneity in soil resources availability and vegetation structure in semi-arid savanna.

Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna

BackgroundThe quantification and spatially explicit mapping of carbon stocks in terrestrial ecosystems is important to better understand the global carbon cycle and to monitor and report change processes, especially in the context of international policy mechanisms such as REDD+ or the implementation of Nationally Determined Contributions (NDCs) and the UN Sustainable Development Goals (SDGs). Especially in heterogeneous ecosystems, such as Savannas, accurate carbon quantifications are still lacking, where highly variable vegetation densities occur and a strong seasonality hinders consistent data acquisition. In order to account for these challenges we analyzed the potential of land surface phenological metrics derived from gap-filled 8-day Landsat time series for carbon mapping. We selected three areas located in different subregions in the central Brazil region, which is a prominent example of a Savanna with significant carbon stocks that has been undergoing extensive land cover conversions. Here phenological metrics from the season 2014/2015 were combined with aboveground carbon field samples of cerrado sensu stricto vegetation using Random Forest regression models to map the regional carbon distribution and to analyze the relation between phenological metrics and aboveground carbon.ResultsThe gap filling approach enabled to accurately approximate the original Landsat ETM+ and OLI EVI values and the subsequent derivation of annual phenological metrics. Random Forest model performances varied between the three study areas with RMSE values of 1.64 t/ha (mean relative RMSE 30%), 2.35 t/ha (46%) and 2.18 t/ha (45%). Comparable relationships between remote sensing based land surface phenological metrics and aboveground carbon were observed in all study areas. Aboveground carbon distributions could be mapped and revealed comprehensible spatial patterns.ConclusionPhenological metrics were derived from 8-day Landsat time series with a spatial resolution that is sufficient to capture gradual changes in carbon stocks of heterogeneous Savanna ecosystems. These metrics revealed the relationship between aboveground carbon and the phenology of the observed vegetation. Our results suggest that metrics relating to the seasonal minimum and maximum values were the most influential variables and bear potential to improve spatially explicit mapping approaches in heterogeneous ecosystems, where both spatial and temporal resolutions are critical.

Influence of abandoned cattle enclosures on plant assemblages and herbivory in a semi‐arid savanna

AbstractIn semi‐arid savannas, abandoned cattle (Bos taurus) enclosures or kraals have been demonstrated to be nutrient‐hotspots for large herbivores. In this study, we examined the interaction between herbivory and forage quality, structure and diversity at 12 kraals (abandoned for 22 years) paired with savanna control plots in Save Valley Conservancy, Zimbabwe. Plant diversity was not different between sites. Herbaceous cover was higher and woody species density lower on abandoned kraals than control plots. Furthermore, abandoned kraals had higher herbaceous productivity and foliar nutrient concentration for grasses than forbs. The abandoned kraals had higher concentration of soil nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), organic carbon (C) compared with control plots and a slightly acidic pH. Grasses were grazed more than forbs on nutrient‐rich abandoned kraals. Only herbaceous biomass for grasses had a significant influence on the interaction between plants and herbivory. No clear relationships were observed between forage quality and herbivory and plant species diversity. Abandoned kraals provide patches of nutrient‐rich forage, increasing savanna heterogeneity and in turn influence grazing patterns of large herbivores, and therefore influence ecosystem functioning. A conservation monitoring programme is recommended on such nutrient‐rich patches as they serve as foraging‐hotspots for herbivores in a dystrophic African semi‐arid savanna.

Termite mounds vary in their importance as sources of vegetation heterogeneity across savanna landscapes

AbstractQuestionsTermite mounds are known to host a suite of unique plants compared with the surrounding savanna matrix. However, most studies testing the significance of mounds for ecosystem heterogeneity have been conducted at single sites. Mound effects on savanna heterogeneity across varying landscapes are less well understood, and how effects might vary across geological types and mounds of different sizes is as yet unknown.LocationGonarezhou National Park, Zimbabwe.MethodsWe studied effects of termite mounds on vegetation spatial heterogeneity across two geologies (granite and basalt), including effects of mound size and the spatial extent of termite influence. Herbaceous vegetation was sampled on mounds and savanna matrix plots, and along distance transects away from mounds. Soil nutrients on mounds and in the matrix were also compared between geologies.ResultsSoil nutrients were more concentrated in large mounds compared with the matrix on granite, but not on basalt, with mounds therefore acting as nutrient hotspots only on nutrient‐poor granite. Large‐ and medium‐sized mounds hosted compositionally different grass species to the matrix on granite, but not on basalt. Large mounds on granite also had significantly lower grass and forb species richness compared with the matrix. However, small mounds on granite, and all mound size categories on basalt, did not have an effect on grass and forb species richness or assemblage composition, an observation attributed to a lack of difference in soil nutrients between the mounds and matrix.ConclusionOur study shows that the significance of termite mounds to ecosystem spatial heterogeneity is strongly influenced by geology and mound size, with mound effects on herbaceous plant species heterogeneity more pronounced in dystrophic geologies and around large mounds. Future studies should take greater cognisance of landscape context and mound size when seeking to understand the contribution of termite mounds to ecosystem structure and function.

A systematic review of elephant impact across Africa

Contradictory findings among scientific studies that address a particular issue may impede the conversion of science to management implementation. A systematic review of peer-reviewed studies to generate a single outcome may overcome this problem. The contentious topic of the impact that a megaherbivore such as the savanna elephant have for other species and their environment can benefit from such an approach. After some 68 years, 367 peer-reviewed papers covered the topic and 51 of these papers provided sufficient data to be included in a meta-analysis. We separated the direct impact that elephants had on trees and herbs from the indirect effects on other vertebrates, invertebrates, and soil properties. Elephants have an impact on tree structure and abundance but no overall negative cascading effects for species that share space with them. Primary productivity explained a small amount of variation of elephant impact on vegetation. Elephant numbers (density), study duration, rainfall, tree cover, and the presence of artificial water and fences failed to describe patterns of impact. We conclude that published information do not support the calls made for artificially manipulating elephant numbers to ameliorate elephant impact, and call for the management of space use by elephants to maintain savanna heterogeneity.

Influence of landscape structure on Euglossini composition in open vegetation environments

Abstract The fauna of Euglossini bees is poorly known in savanna regions, making it difficult to understand how these bees use open vegetation environments. The aim of this study was to evaluate the influence of landscape structure on species abundance and composition of Euglossini bees in naturally heterogeneous savanna landscapes. Nine sites were sampled monthly using six traps with chemical baits. Three aromatic essences (eucalyptol, methyl salicylate and vanillin) were used to attract the Euglossini. Surrounding environmental conditions were measured using three independent variables, calculated in multiple scales: index of local vegetation and two landscape indices (Shannon Diversity and area-weighted shape). We compared the competing hypotheses through model selection based on Second-order Akaike Information Criterion (AICc). The four competing hypothesis were: (1) The local vegetation complexity favors Euglossini bees species richness and/or abundance (local vegetation hypothesis); (2) The proportion of the native vegetation types favors Euglossini bees species richness and/or abundance (habitat amount hypothesis); (3) Higher landscape diversity shall increase species richness of Euglossini bees (landscape heterogeneity hypothesis); (4) More complex landscape configuration shall favor the Euglossini bees richness and/or abundance (landscape heterogeneity hypothesis). We sampled 647 individuals belonging to six species of two distinct genera. Our results support the habitat amount hypothesis since bees’ abundance was strongly related with the proportion of habitat in the surrounding landscape. This may be related to the availability of floral and nesting resources in some types of savanna vegetation.

Spatial Modeling of Lidar-Derived Woody Biomass Estimates Collected Along Transects in a Heterogeneous Savanna Landscape

Waveforms from the Ice, Cloud and land Elevation Satellite have successfully estimated footprint-level canopy height and aboveground biomass even in structurally complex savanna ecosystems. However, at the landscape level wall-to-wall maps are preferred since they are more easily integrated with other geospatial data products. We evaluated and compared the utility of inverse distance weighting, cokriging, regression kriging and image segmentation methods to create wall-to-wall maps from footprint-level estimates of biomass across a 13 600-Ha Oak savanna landscape in Santa Clara county, California. The four methods estimated biomass with between 39% (inverse distance weighting) and 66% (image segmentation) of variance explained and RMSE of 42% and 32% of the mean, respectively. When more waveforms were available across or along track to characterize patch biomass with the image segmentation method, 78% of variance in biomass was explained (RMSE = 21% of the mean). Overall, the mean biomass estimated by the four methods did not differ significantly but a visual inspection of the output maps showed marked differences in the ability of each model to mimic the primary variable's landscape-level trend. We conclude that transects of lidar data can be used to create wall-to-wall biomass maps in savannas but the methods require a higher sampling intensity and informative auxiliary data to reproduce the variability of the biomass across the landscape. We recommend that future satellite lidar missions increase the sampling intensity across track so that biomass observations are made and characterized at the scale at which they vary

Mammalian herbivores, grass height and rainfall drive termite activity at different spatial scales in an African savanna

AbstractTermites have a large influence on ecosystem functioning. Understanding what drives termite activity patterns improves understanding of nutrient cycling, productivity, and heterogeneity in savannas. We present a mechanistic framework that relates the interactive effects of rainfall, grassland structure, large herbivore presence, and soil factors to termite activity. To test this framework, we used grass litterbags to monitor termite activity at ten sites across Hluhluwe‐iMfolozi Park, South Africa. We assessed the effects of abiotic and biotic factors on termite activity at two scales: the large (landscape) scale, variation in bait removal among 300 m2 plots that were distributed across the park and at the small (within‐plot) scale (1–300 m2). Half of our sites were located inside large herbivore exclosures to test for the effect of mammalian herbivore presence. At the landscape scale, termite grass removal declined towards higher rainfall and in the presence of mammalian herbivores. Removal did not depend on soil factors. At the small scale, removal declined with increasing grass height, particularly in the 1 m surrounding the bait bag. Resource quality did not affect bait removal. We suggest that competition for forage drives the negative effect of mammalian herbivores on termites, whereas lower bait removal in taller swards may be due to direct negative effects from rainfall, fire and/or competition with free‐living microbes. Ultimately, we suggest that the impact of termites on nutrient cycling is most pronounced when abiotic (rainfall) and biotic conditions (mammalian herbivory) limit grass removal by fire and decomposition by free‐living microbes.

Plot-level aboveground woody biomass modeling using canopy height and auxiliary remote sensing data in a heterogeneous savanna

Remote sensing studies aiming at assessing woody biomass have demonstrated a strong relationship between canopy height and plot-level aboveground biomass, but most of these studies focused on closed canopy forests. To date, a few studies have examined the strength and reliability of this relationship using large footprint lidar in savannas. Furthermore, there have been few studies of appropriate methods for the comparison of models that relate aboveground biomass to canopy height metrics without consideration of variation in species composition (generic models) to models developed for individual species composition or vegetation types. We developed generic models using the classical least-squares regression modeling approach to relate selected canopy height metrics to aboveground woody biomass in a savanna landscape. Hierarchical Bayesian analysis (HBA) was then used to explore the implications of using generic or composition-specific models. Our study used the estimates of aboveground biomass from field data, canopy height estimates from airborne discrete return lidar, and a proxy for canopy cover (the Normalized Difference Vegetation Index) from Landsat 5 Thematic Mapper data, collected from the oak savannas of Tejon Ranch Conservancy in Kern County, California. Models were developed and analyzed using estimates of canopy height and aboveground biomass calculated at the level of 50-m diameter plots, comparable with footprint diameter of existing large footprint spaceborne lidar data. The two generic models that incorporated canopy cover proxies performed better than one model that did not use canopy cover information. From the HBA, we found out that for all models both the intercept and slope had interspecific variability. The valley oak dominated plots consistently had higher slopes and intercepts, whereas the plots dominated by blue oaks had the lowest. However, the intercept and slope values of the composition-specific models did not differ much from the generic (overall) values and their 95% credible intervals (CIs) overlapped the generic mean values. We conclude that the narrow range of the distribution and the overlap of the CIs of the composition-specific and generic parameters suggest that scaling rules do exist for savannas. The distribution of the posterior densities of the differences between composition-level and generic-level parameter values showed high support for the use of generic parameters, suggesting that all three models are applicable across the range of compositions in this study. Therefore, in this case, the choice of method depends more on secondary considerations such as data availability and scale of analyses.

Browsing intensity of herbaceous forbs across a semi-arid savanna catenal sequence

Ecological models to explain savanna heterogeneity and functioning weakly represent herbaceous forbs, which inevitably created knowledge gaps regarding the diversity and ecology of forbs. Forbs constitute over 70% of semi-arid savanna species richness. The aim of this study was therefore to (i) identify forb species that potentially form part of herbivore diet, and (ii) determine forb browsing intensity across a granitic savanna catenal sequence. Localized contrasts between nutrient-poor dystrophic uplands and nutrient-rich eutrophic bottomlands led us to predict that bottomlands would host forb assemblages dominated by browsed species. Forbs contributed to 78% of the total herbaceous layer species richness, of which 43% were browsed at varying intensities along the catenal sequence. Each topographic zone hosted a unique assemblage of browsed forbs, although browsing intensity was highest in the sodic bottomlands. Some browsed forbs formed prostrate, stoloniferous covers on the bottomlands, creating a lawn type dominated by forbs. Results presented here are the first to identify browsed forb species and to illustrate the turnover in browsing intensity of forbs across a semi-arid savanna catenal sequence. Our results add weight to the need to include herbaceous forbs in savanna management models to better understand savanna resource ecology and heterogeneity.

Comparing the Dry Season In-Situ Leaf Area Index (LAI) Derived from High-Resolution RapidEye Imagery with MODIS LAI in a Namibian Savanna

The Leaf Area Index (LAI) is one of the most frequently applied measures to characterize vegetation and its dynamics and functions with remote sensing. Satellite missions, such as NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) operationally produce global datasets of LAI. Due to their role as an input to large-scale modeling activities, evaluation and verification of such datasets are of high importance. In this context, savannas appear to be underrepresented with regards to their heterogeneous appearance (e.g., tree/grass-ratio, seasonality). Here, we aim to examine the LAI in a heterogeneous savanna ecosystem located in Namibia’s Owamboland during the dry season. Ground measurements of LAI are used to derive a high-resolution LAI model with RapidEye satellite data. This model is related to the corresponding MODIS LAI/FPAR (Fraction of Absorbed Photosynthetically Active Radiation) scene (MOD15A2) in order to evaluate its performance at the intended annual minimum during the dry season. Based on a field survey we first assessed vegetation patterns from species composition and elevation for 109 sites. Secondly, we measured in situ LAI to quantitatively estimate the available vegetation (mean = 0.28). Green LAI samples were then empirically modeled (LAImodel) with high resolution RapidEye imagery derived Difference Vegetation Index (DVI) using a linear regression (R2 = 0.71). As indicated by several measures of model performance, the comparison with MOD15A2 revealed moderate consistency mostly due to overestimation by the aggregated LAImodel. Model constraints aside, this study may point to important issues for MOD15A2 in savannas concerning the underlying MODIS Land Cover product (MCD12Q1) and a potential adjustment by means of the MODIS Burned Area product (MCD45A1).

Legacy Effects of Different Land-Use Histories Interact with Current Grazing Patterns to Determine Grazing Lawn Soil Properties

Pastoralism and agriculture have affected rangeland ecosystems over the past millennia, including many ecosystems that are currently protected as reserves. However, the legacy of these land-use practices on current ecosystem functioning remains unclear. We studied legacy effects of former human land use on soil physical and chemical properties in a South African savanna. We did this by comparing soil properties in grazing lawns (patches of short grass maintained by the positive feedback between grazing intensity and forage quality) with the surrounding less grazed bunch grasslands within three different human land-use history contexts: (i) Abandoned bomas: permanent stone enclosures where livestock were kept overnight, and dung and urine accumulated for several years or decades. (ii) Old fields: areas where vegetation was cleared, soil tilled, and cultivated, but received little or no fertilization. (iii) Natural grasslands: not cultivated but grazed by livestock before the establishment of the reserve and wildlife thereafter. Former human land use rather than soil texture was the main determinant of grazing lawn location. Moreover, lawn soil properties also varied among land-use histories. In all grazing lawns, soil nutrient concentrations were higher than in adjacent grasslands but abandoned bomas contained three times more phosphorus, and twice as much nitrogen and carbon than old fields and natural grassland lawns. In addition to past land use, soil texture influenced lawn soil nutrients: Concentrations of phosphorus, potassium, calcium, magnesium, total nitrogen, and carbon in lawns were higher on clayey soils than sandy soils, whereas phosphorus, C:N ratio, and pH did not change with soil texture. Our study confirms previous findings on the effect of human land use on savanna heterogeneity, but also highlights how legacy effects may vary among different historic land-use practices.