Asynchronous food-web pathways could buffer the response of Serengeti predators to El Niño southern oscillation

Understanding how entire ecosystems maintain stability in the face of climatic and human disturbance is one of the most fundamental challenges in ecology. Theory suggests that a crucial factor determining the degree of ecosystem stability is simply the degree of synchrony with which different species in ecological food webs respond to environmental stochasticity. Ecosystems in which all food-web pathways are affected similarly by external disturbance should amplify variability in top carnivore abundance over time due to population interactions, whereas ecosystems in which a large fraction of pathways are nonresponsive or even inversely responsive to external disturbance will have more constant levels of abundance at upper trophic levels. To test the mechanism underlying this hypothesis, we used over half a century of demographic data for multiple species in the Serengeti (Tanzania) ecosystem to measure the degree of synchrony to variation imposed by an external environmental driver, the El Niño Southern Oscillation (ENSO). ENSO effects were mediated largely via changes in dry-season vs. wet-season rainfall and consequent changes in vegetation availability, propagating via bottom-up effects to higher levels of the Serengeti food web to influence herbivores, predators and parasites. Some species in the Serengeti food web responded to the influence of ENSO in opposite ways, whereas other species were insensitive to variation in ENSO. Although far from conclusive, our results suggest that a diffuse mixture of herbivore responses could help buffer top carnivores, such as Serengeti lions, from variability in climate. Future global climate changes that favor some pathways over others, however, could alter the effectiveness of such processes in the future

Data from: Litter type and termites regulate root decomposition across contrasting savanna land-uses

Decomposition is a vital ecosystem process, increasingly modified by human activity. Theoretical frameworks and empirical studies that aim to understand the interplay between human land-use, macro-fauna and decomposition processes have primarily focused on leaf and wood litter. For a whole-plant understanding of how land-use and macro-fauna influence decomposition, investigating root litter is required. Using litterbags, we quantified rates of root decomposition across contrasting tropical savanna land-uses, namely wildlife and fire-dominated protected areas and livestock pastureland without fire. By scanning litterbags for termite intrusion, we differentiated termite and microbial driven decomposition. Root litter was buried underneath different tree canopies (leguminous and non-leguminous trees) and outside canopies to account for savanna landscape effects. Additionally, we established a termite cafeteria-style experiment and common garden to explore termite selectivity of root litter and root trait relationships, respectively. After one year, we found no significant differences in root litter mass loss between wildlife dominated areas and pastureland. Instead, we found consistent species differences in root litter mass loss across land-uses and additive and non-additive effects of termites on root decomposition across plant species. Termite selectivity for root litter species occurred for both root and leaf litter buried near termite mounds, but was not explained by root traits measured in the common garden. Termite foraging was greater under leguminous tree canopies than other canopies; however, this did not influence rates of root decomposition. Our study suggests that land-use has a weak direct effect on belowground processes in savannas. Instead, changes in herbaceous species composition and termite foraging have stronger impacts on belowground decomposition. Moreover, termites were not generalist decomposers of root litter, but their impact varies depending on plant species identity and likely associated root traits. This root litter selectivity by termites is likely to be an important contributor to spatial heterogeneity in savanna nutrient cycling

Contrasting wildlife and livestock impacts on plant biomass dynamics inside and outside the Serengeti National Park, Tanzania

African savannahs represent one of the world's most productive ecosystems and one of the last vestiges of diverse wild herbivore populations. Accompanying human population growth, livestock is replacing wildlife as the dominant herbivore. To understand the impact of this shift in herbivore assemblage, we established a network of exclosures across a rainfall gradient contrasting pastoral and wildlife management around the Serengeti National Park, Tanzania. Small-scale exclosures and open plots were established underneath leguminous and non-leguminous trees and outside canopies to account for the landscape structure. Every month herbaceous biomass was estimated non-destructively in 128 plots using a calibrated pasture disc. For each site, monthly herbivore dung surveys were used to estimate the herbivore assemblage. Additionally, plant community composition was surveyed and root biomass determined via ingrowth cores. After the first year, aboveground biomass was most strongly associated with plant species composition with greater production for communities in the drier region. On average, the greatest aboveground biomass occurred inside exclosures underneath leguminous trees. Meanwhile, root biomass production was highest in the wetter region suggesting a shift of investment from belowground than aboveground across the rainfall gradient. Herbivore assemblage varied in space and time, but did not consistently influence aboveground biomass accrual. Thus, our results suggest plant biomass production across wild and domestic herbivore assemblages relates mainly to plant species composition and these species' adaption to climate variability.  We discuss our results in the context of a changing savannah landscape involving people, wildlife and climate seasonality.peerReviewe

Comparative Assessment of UAV and Sentinel-2 NDVI and GNDVI for Preliminary Diagnosis of Habitat Conditions in Burunge Wildlife Management Area, Tanzania

Habitat condition is a vital ecological attribute in wildlife conservation and management in protected areas, including the Burunge wildlife management areas in Tanzania. Traditional techniques, including satellite remote sensing and ground-based techniques used to assess habitat condition, have limitations in terms of costs and low resolution of satellite platforms. The Normalized Difference Vegetation Index (NDVI) and Green NDVI (GNDVI) have potential for assessing habitat condition, e.g., forage quantity and quality, vegetation cover and degradation, soil erosion and salinization, fire, and pollution of vegetation cover. We, therefore, examined how the recently emerged Unmanned Aerial Vehicle (UAV) platform and the traditional Sentinel-2 differs in indications of habitat condition using NDVI and GNDVI. We assigned 13 survey plots to random locations in the major land cover types: three survey plots in grasslands, shrublands, and woodlands, and two in riverine and mosaics cover types. We used a UAV-mounted, multi-spectral sensor and obtained Sentinel-2 imagery between February and March 2020. We categorized NDVI and GNDVI values into habitat condition classes (very good, good, poor, and very poor). We analyzed data using descriptive statistics and linear regression model in R-software. The results revealed higher sensitivity and ability of UAV to provide the necessary preliminary diagnostic indications of habitat condition. The UAV-based NDVI and GNDVI maps showed more details of all classes of habitat conditions than the Sentinel-2 maps. The linear regressions results showed strong positive correlations between the two platforms (p < 0.001). The differences were attributed primarily to spatial resolution and minor atmospheric effects. We recommend further studies to test other vegetation indices

Spatio-Temporal Changes in Wildlife Habitat Quality in the Greater Serengeti Ecosystem

Understanding habitat quality and its dynamics is imperative for maintaining healthy wildlife populations and ecosystems. We mapped and evaluated changes in habitat quality (1975–2015) in the Greater Serengeti Ecosystem of northern Tanzania using the Integrated Valuation of Environmental Services and Tradeoffs (InVEST) model. This is the first habitat quality assessment of its kind for this ecosystem. We characterized changes in habitat quality in the ecosystem and in a 30 kilometer buffer area. Four habitat quality classes (poor, low, medium and high) were identified and their coverage quantified. Overall (1975–2015), habitat quality declined over time but at rates that were higher for habitats with lower protection level or lower initial quality. As a result, habitat quality deteriorated the most in the unprotected and human-dominated buffer area surrounding the ecosystem, at intermediate rates in the less heavily protected Wildlife Management Areas, Game Controlled Areas, Game Reserves and the Ngorongoro Conservation Area and the least in the most heavily protected Serengeti National Park. The deterioration in habitat quality over time was attributed primarily to anthropogenic activities and major land use policy changes. Effective implementation of land use plans, robust and far-sighted institutional arrangements, adaptive legal and policy instruments are essential to sustaining high habitat quality in contexts of rapid human population growth

Teatime in the Serengeti:macrodetritivores sustain recalcitrant plant litter decomposition across human-modified tropical savannahs

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The central African soil spectral library: a new soil infrared repository and a geographical prediction analysis

Open Access Journal; Published online: 26 Oct 2021Information on soil properties is crucial for soil preservation, the improvement of food security, and the provision of ecosystem services. In particular, for the African continent, spatially explicit information on soils and their ability to sustain these services is still scarce. To address data gaps, infrared spectroscopy has achieved great success as a cost-effective solution to quantify soil properties in recent decades. Here, we present a mid-infrared soil spectral library (SSL) for central Africa (CSSL) that can predict key soil properties, allowing for future soil estimates with a minimal need for expensive and time-consuming wet chemistry. Currently, our CSSL contains over 1800 soil samples from 10 distinct geoclimatic regions throughout the Congo Basin and along the Albertine Rift. For the analysis, we selected six regions from the CSSL, for which we built predictive models for total carbon (TC) and total nitrogen (TN) using an existing continental SSL (African Soil Information Service, AfSIS SSL; n=1902) that does not include central African soils. Using memory-based learning (MBL), we explored three different strategies at decreasing degrees of geographic extrapolation, using models built with (1) the AfSIS SSL only, (2) AfSIS SSL combined with the five remaining central African regions, and (3) a combination of AfSIS SSL, the remaining five regions, and selected samples from the target region (spiking). For this last strategy we introduce a method for spiking MBL models. We found that when using the AfSIS SSL only to predict the six central African regions, the root mean square error of the predictions (RMSEpred) was between 3.85–8.74 and 0.40–1.66 g kg−1 for TC and TN, respectively. The ratio of performance to the interquartile distance (RPIQpred) ranged between 0.96–3.95 for TC and 0.59–2.86 for TN. While the effect of the second strategy compared to the first strategy was mixed, the third strategy, spiking with samples from the target regions, could clearly reduce the RMSEpred to 3.19–7.32 g kg−1 for TC and 0.24–0.89 g kg−1 for TN. RPIQpred values were increased to ranges of 1.43–5.48 and 1.62–4.45 for TC and TN, respectively. In general, predicted TC and TN for soils of each of the six regions were accurate; the effect of spiking and avoiding geographical extrapolation was noticeably large. We conclude that our CSSL adds valuable soil diversity that can improve predictions for the Congo Basin region compared to using the continental AfSIS SSL alone; thus, analyses of other soils in central Africa will be able to profit from a more diverse spectral feature space. Given these promising results, the library comprises an important tool to facilitate economical soil analyses and predict soil properties in an understudied yet critical region of Africa. Our SSL is openly available for application and for enlargement with more spectral and reference data to further improve soil diagnostic accuracy and cost-effectiveness

Supplement 1. R code for computer simulation model of time dynamics of predator and two prey species shown in Fig. 1.

<h2>File List</h2><div> <a href="Rcode_for_model_simulations_Figure_1.txt">Rcode_for_model_simulations_Figure_1.txt</a> (md5: 338340b55f076dac7ccd48545c681460)</div><h2>Description</h2><div> <p>This R code text file simulates time dynamics of predator and two prey species to produce the outcomes shown in Fig. 1 of the paper. State variable names and parameters are exactly as depicted in the text. The code is straighforward and carefully annotated to explain objectives of each section. </p> </div