Feedback of trees on nitrogen mineralization to restrict the advance of trees in C4 savannahs
Steven I. Higgins1, Moagi Keretetse2 and Edmund C. February2 1
Cite this article: Higgins SI, Keretetse M, February EC. 2015 Feedback of trees on nitrogen mineralization to restrict the advance of trees in C4 savannahs. Biol. Lett. 11: 20150572. http://dx.doi.org/10.1098/rsbl.2015.0572
Received: 3 July 2015 Accepted: 22 July 2015
Department of Botany, University of Otago, PO Box 56, Dunedin 9016, New Zealand Department of Biological Sciences, University of Cape Town, Private Bag, Rondebosch 7701, South Africa Remote sensing studies suggest that savannahs are transforming into more tree-dominated states; however, progressive nitrogen limitation could potentially retard this putatively CO2-driven invasion. We analysed controls on nitrogen mineralization rates in savannah by manipulating rainfall and the cover of grass and tree elements against the backdrop of the seasonal temperature and rainfall variation. We found that the seasonal pattern of nitrogen mineralization was strongly influenced by rainfall, and that manipulative increases in rainfall could boost mineralization rates. Additionally, mineralization rates were considerably higher on plots with grasses and lower on plots with trees. Our findings suggest that shifting a savannah from a grass to a tree-dominated state can substantially reduce nitrogen mineralization rates, thereby potentially creating a negative feedback on the CO2-induced invasion of savannahs by trees.
Electronic supplementary material is available at http://dx.doi.org/10.1098/rsbl.2015.0572 or via http://rsbl.royalsocietypublishing.org.
1. Introduction C4 savannahs are ecosystems characterized by a continuous layer of C4 grasses and a discontinuous layer of fire tolerant, yet shade-intolerant trees . Recent analyses of aerial photographs have suggested that some savannah ecosystems are undergoing dramatic shifts towards increased tree cover [2,3]. These shifts have been attributed the different photosynthetic responses of trees (C3) and savannah grasses (C4) to atmospheric CO2 fertilization. Specifically, C4 plants are CO2-saturated at ambient atmospheric CO2 levels, whereas C3 plants are not . Furthermore, savannah trees have, relative to grasses, a carbon demanding life history which ensures that trees can benefit from increased carbon assimilation . The CO2 fertilization hypothesis has also been supported by experimental  and modelling studies [7,8]. An argument against the CO2 fertilization hypothesis is that elevated CO2 may stimulate a short-term increase in photosynthesis, but that nitrogen will ultimately limit a sustained increase in photosynthesis . Evidence for this progressive nitrogen limitation hypothesis is, however, not universal . Indeed, recent analyses suggest that the impacts of atmospheric CO2 fertilization on plant growth are contingent on the availability of co-limiting factors such as nitrogen and water [11,12]. The rate at which nitrogen is made available to plants by soil nitrogen mineralization is influenced by temperature, moisture and the quality of the substrate . The temperature increases expected in savannahs will stimulate mineralization rates . Rainfall changes are more uncertain; in our study area rainfall event size is increasing, but event frequency is decreasing . Substrate amount and quality will change as tree– grass ratios shift, but the direction of such shifts are unclear . However, should increasing tree dominance lead to an overall decrease in litter quality, we would expect decomposition and mineralization rates to decrease [17,18]. Such decreases would, in turn, act as a negative feedback on CO2-fertilized advances of trees in savannahs [5,8].
& 2015 The Author(s) Published by the Royal Society. All rights reserved.
bare ground grass only tree only grass and tree
0.6 0.4 0.2
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nitrogen mineralization rate (mg-N g-soil–1 d–1)
nitrogen mineralization rate (mg-N g-soil–1 d–1)
rainfall reduction rainfall control rainfall addition
0.8 0.6 0.4 0.2 0
Dec Jan time (months)
Figure 1. Mean monthly nitrogen mineralization rates separated by vegetation cover categories (a) and by rainfall manipulation treatments (b). Error bars represent standard errors of the mean. To explore this hypothesis, we estimated nitrogen mineralization rates over a growing season in a savannah ecosystem. We recorded mineralization rates on plots with different combinations of grasses and trees and hence different inputs of the substrates for mineralization. We additionally manipulated the rainfall input onto these plots using rain-out shelters and a passive irrigation system.
2. Methods The study site is located in the Kruger National Park, South Africa. Mean annual temperature is 22.48C and mean annual precipitation is 745 mm. Rain falls in summer. The vegetation is droughtdeciduous and classified as granite lowveld savannah. The soils are ca 100 cm deep red loamy sands derived from granite, gneiss and migmatite. The soils have sand, silt and clay contents of 93%, 6% and 1%, pH of 3.3, C and N contents of 3.3 and 0.52 mg kg21 and P (Bray II) contents of 3.23 mg kg21 . Rates of nitrogen deposition in the region are 21.6 kg ha21 yr21 . The study plots were located in a fenced animal exclosure near Pretoriuskop (25.1298 S, 31.2338 E). Plots were 4 4 m in size. The experiment included treatments with bare ground, tree only, tree and grass and grass only. Treatments with trees were centred on single-stemmed Terminalia sericea individuals. Rainfall was manipulated with a passive irrigation scheme that simulated three rainfall levels, ambient, 50% of ambient and 150% of ambient. The timing of rainfall was not manipulated. Each treatment combination was replicated six times. See the electronic supplementary material for more details on the experimental design.
Net nitrogen mineralization rates were determined at the end of each month from September to March using the in situ core field incubation method as described in the electronic supplementary material. Nitrogen mineralization rates were analysed using a linear mixed-effects model that we estimated using Markov chain Monte Carlo methods as described in the electronic supplementary material.
3. Results and discussion A plot of the monthly mean mineralization rates, grouped by vegetation cover (figure 1a) and rainfall manipulation (figure 1b) indicates that mineralization rates are low from August to October, high in November after which they decrease towards April. The statistical analysis of these data revealed that mineralization rates were strongly influenced by rainfall manipulation: rainfall addition plots had rates 0.15 mg-N g-soil21 d21 higher than rainfall reduction plots (figure 2a). This rainfall effect is consistent with the dependency of mineralization on soil moisture . Of the environmental covariates (figure 2b – d), monthly rainfall had the strongest effect on mineralization rates (figure 2b). Months that receive approximately 100 mm are expected to have rates 0.8 mg-N g-soil21 d21 higher than months receiving less than 10 mm of rainfall. The temperature range experienced in our study had an insignificant effect on mineralization rates (figure 2c), with the statistical model attributing a less than 0.1 mg-N g-soil21 d21 change in mineralization rate to a 58C change in monthly mean
effect on nitrogen mineralization rate (mg-N g-soil−1 d−1)
50 100 150 monthly rainfall (mm)
(d ) 1.0
22 23 temperature (°C)
Aug Sep Oct Nov Dec Jan Feb Mar Apr time (months)
Figure 2. The effect of (a) grass presence, tree presence and rain manipulation, (b) monthly rainfall, (c) monthly temperature, and (d) time on nitrogen mineralization rates. In (a), the open rectangles span the 95% credible intervals and the closed rectangles span the 50% credible intervals of the estimates. In (b – d), the solid line is the mean estimate and the grey outer envelope denotes the 95% credible intervals of the estimated effects.
temperature. Mineralization rates decreased linearly with time, with time accounting for ca 0.2 mg-N g-soil21 d21 difference in the initial and final mineralization rates (figure 2d). This time effect is expected, because through time preferential mineralization of more labile material causes the substrate to become dominated by less labile material, which lowers the mineralization rates . The effect of rainfall on mineralization rates was nonlinear with rainfall stimulating mineralization rates from 0 to 100 mm per month, but those months receiving more than 100 mm had progressively lower mineralization rates (figure 2c). Higher rainfall might induce anaerobic soil conditions which would inhibit mineralization , however this is unlikely for the well-drained soils in this study where soil moisture probes never indicated saturated soils . Mineralization rates were on average 0.10 mg-N g-soil21 d21 higher when grasses were present and 0.05 mg-N g-soil21 d21 lower when trees were present (figure 2a). This finding supports the hypothesis posed in the introduction that
increased tree cover could induce decreased mineralization rates. This finding would be expected if substrate quality was lower in tree-dominated situations. Several lines of evidence support this proposition. First, grass leaf decomposition rates are higher (exponential decay constants of 0.01–0.25 ) than those of trees (exponential decay constants of 0.005–0.07 ) in the study region. Our unpublished data from this study site report that leaf C : N ratios are similar for common grass species (C : N ¼ 27) and our study tree species (C : N ¼ 30), but the C : N ratios of the root material of common grasses (C : N ¼ 62) are lower than those of the study tree species (C : N ¼ 95, electronic supplementary material, table S2 ). The C : N ratio of organic material at the soil surface and in the upper 5 cm of the soil profile at this study site was also lower between tree canopies than under tree canopies (F2,16 ¼ 7.457, p , 0.0148; electronic supplementary material, table S3 ). Furthermore, fine root biomass was lower under tree canopies than between tree canopies at this study site .
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effect on nitrogen mineralization rate (mg-N g-soil−1 d−1)
Data accessibility. Data are available in the electronic supplementary material.
Authors’ contributions. E.C.F. and S.I.H. designed the study and wrote the manuscript. M.K., E.C.F. and S.I.H. conducted the field and laboratory research. S.I.H. analysed the data. All authors provided intellectual input and edited/approved the manuscript.
Competing interests. We declare we have no competing interests. Funding. This research was supported by the Andrew Mellon Foundation and the Deutsche Forschungsgemeinschaft (DFG).
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Taken together, this suggests that tree-dominated situations in this study had input of less litter and this litter was of lower N content, which is typically associated with lower mineralization rates . Our study therefore provides evidence for a negative feedback of trees on nitrogen availability that might serve to slow rates of CO2-fuelled shifts to tree dominance in savannahs. Several issues may limit the generality of our findings. First, in other systems, trees produce higher litter quality than grasses which over time enhance the fertility of these sites [25,26]. Soil N availability has also been shown to decrease when C4 vegetation replaces C3 vegetation . Moreover, in our study region, Dichrostachys cinerea (a common woody shrub) has a fine root C : N ratio of 29, which is lower than the C : N ratio of the grasses in our sample (electronic supplementary material, table S2 ). In systems encroached by Dichrostachys, we might therefore expect a positive feedback on nitrogen cycling as opposed to the negative feedback we report for our Terminalia-invaded system. Second, elevated CO2 may enhance the photosynthetic nutrient use efficiency’s (PNUE) of C3 plants more than that of C4 plants , suggesting that they might cope better with a low fertility future. However, the PNUE of C4 plants is considerably higher than that of C3 plants, and it is unlikely that the PNUE of C3 plants under future CO2 conditions will exceed those of C4 plants . Third, increasing tree cover will be associated with increased transpiration and consequently reduced soil moisture availability , but in semi-arid savannahs higher grass dominance has been associated with higher root biomass and lower soil moisture availability [21,24]. Soil moisture effects are further
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