At higher resource levels, some of the water accumulated by grasses below them became available Savvanna model trees as well, and thus trees could persist together with grasses in their same patterns. In the originally published version of this Article, reference 23 did not refer to the correct paper. Sankaran et al. Although widely debated in literature, kodel mechanisms behind their coexistence are not yet clear e. However, when looking at the areas distant from human influence Fig.
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Category: Publications. The high dependence of humans on the landscape, through agricultural production, tourism, and natural resource extraction makes understanding savanna vegetation dynamics essential. Studies analyzing resilience of savannas suggest potential state changes in vegetation structure from continuous grasslands with sporadic woody cover to less biologically productive landscapes. One of the biggest questions in this landscape is the impact of climate change.
Manipulating climate inputs and management regimes allowed us to analyze the resilience of savanna vegetation under multiple Intergovernmental Panel on Climate Change IPCC scenarios. Trends in future climate indicate an increase in temperatures greater than 1.
Model results indicate a long-term decrease in multiple size classes of vegetation across all the four land systems. However, the model runs show differing response to climate change between the woody and herbaceous cover types. Spatial trends across the park follow closely with the north-south climate gradient.
The most spatially distinct land system was Skukuza, which exhibited some of the highest initial net primary production NPP values and also the greatest decreases in NPP into the future. While this region is projected to lose large proportions of its herbaceous and shrub vegetation it is projected to increase in tree green leaf, mostly related to increasing fine leaf vegetation Acacia sp.
The northern land systems were already dominated by mopane, but under all model scenarios mopane will increase in Letaba and Phalaborwa. This mopane increase will offset the loss of herbaceous and shrub vegetation, resulting in little to no decrease in NPP across time for these land systems. This work illustrates that landscape resilience is not only impacted by the severity of changing climate but the degree to which we manage such systems. Read the full publication at Ecological Modelling.
Socializing the rain: human adaptation to ecological variability in a fishery, Mweru-Luapula, Zambia Aug 23rd,
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Here we subdivide satellite vegetation data into those from human-unaffected areas and those from regions close to human-cultivated zones. Bimodality is found to be almost absent in the unaffected regions, whereas it is significantly enhanced close to cultivated zones.
Assuming higher logging rates closer to cultivated zones and spatial diffusion of fire, our spatiotemporal mathematical model reproduces these patterns. Given a gradient of climatic and edaphic factors, rather than bistability there is a predictable spatial boundary, a Maxwell point, that separates regions where forest and savanna states are naturally selected. While bimodality can hence be explained by anthropogenic edge effects and natural spatial heterogeneity, a narrow range of bimodality remaining in the human-unaffected data indicates that there is still bistability, although on smaller scales than claimed previously.
International climate negotiations include a focus on reduced greenhouse gas emissions from tropical deforestation and the need for sustainable forest management to enhance sinks. Such forest management is complicated by the potential existence of tipping points 1 in tropical forests beyond which they may experience abrupt transitions 2 , 3 , 4 to savannas and provide feedbacks to climate change 5.
In the Amazon basin, there exists evidence for tipping points related to two types of feedbacks. First, simulation and modelling studies have shown that hydrological feedbacks could cause basin-scale alternative stable vegetation states 6 , 7 , 8 , 9. The focus of this paper is on a second fire-related feedback that has been linked to forest—savanna bistability in the tropics 10 , 11 , Fire spread requires a spatially well-connected herbaceous layer that occurs only below a certain tree cover threshold; below this threshold, fire spread opens up the canopy more, promoting yet better fire spread Evidence for this process was found empirically, via higher fire frequencies and a bimodal see Methods distribution of tropical tree cover for a certain range of rainfall and seasonality.
A typical characteristic of bistable systems is hysteresis, and in this context it means that the tipping point of rainfall where savannas are converted to forest is higher than that where forests are converted to savannas. In the rainfall range between these two tipping points tree cover would then be observed to have a bimodal distribution because both states are possible here.
Simulations of a simple model of bistable tree cover have shown that spatial heterogeneity can enlarge the observed rainfall range of bimodality This leads to the question of how much of the observed bimodality is due to hysteresis and how much is due to spatial heterogeneity associated with independent variation of other variables that affect stability, such as seasonality, soils and human impact.
This question is especially important as previous studies inferred a model of bistability that only focused on the effect of average rainfall 10 , 11 , 12 , 13 , 14 , If much of the observed bimodality turns out to be due to spatial heterogeneity, hysteresis and bistability may in fact be absent or at least be limited to smaller spatial scales than previously assumed.
Models have reproduced fire-induced forest—savanna bistability by parameterizing or simulating the fire-vegetation feedback 13 , 15 , 16 , or by only assuming a specific dependence of model parameters on regional climate Yet, previous theoretical 18 , 19 studies have shown that when allowing spatial interaction, hysteresis and bimodality disappear; instead, there is an environmentally determined boundary that separates both states.
Only at this boundary, coined the Maxwell point MP , both states coexist. Above the MP, one state dominates while below the MP, the other dominates. Recently, evidence has been found for this phenomenon in satellite data 20 of tree cover. Still, no empirically testable spatiotemporal model including the combined effect of all natural and human influences has been proposed.
In the Amazon region, a gradient of rainfall runs from the dry southeast, where the dry season can have considerable length, to the wet northwest, where dry season is short or nonexistent 7 , 21 Supplementary Fig. Natural vegetation follows this climatic gradient.
From southeast to northwest, there are dry savannas, moist savannas and eventually tropical forest. Human impact occurs along the same gradient, with drier areas in the southeast having been subject to more land-use change than the wetter northwest.
The Amazon region is a good starting point to study human impact effects since the implications of deforestation and logging are well studied there and since there are fewer confounding factors than, for example in Africa, where presence of large herbivores are known to have an important additional effect on vegetation It has been demonstrated both empirically 4 and with theoretical models 23 , 24 , 25 that human impact can significantly alter the stability and resilience of ecosystems.
Human impact in the Amazon region encompasses both direct deforestation and various edge effects around cleared areas such as changes in forest structure, tree mortality, forest microclimate and biodiversity Deforestation comprises both clearcutting, the conversion of forested land to food crops or pastures and selective logging, the removal of only marketable tree species Both logged forests and edges of clearcut provide decreased transpiration rates and thus lower atmospheric humidity that, along with scattered wood debris, makes them highly susceptible to fire 28 , After being burnt once, nearby forest fragments become yet more susceptible to fire While previous empirical studies recognize that human impact can influence forest stability, they either focused on bistability in natural systems by excluding affected areas from the analysis 10 or did not explicitly take human impact into account In this work, we examine human impact on Amazonian forest—savanna bistability.
Our key methodology involves three steps. First, we set up a statistical model that predicts pre-human forest cover from average rainfall, rainfall seasonality and soils.
Second, we analyse how human impact affects bimodality of tree cover by considering separately areas that are close to and areas that are far from human influence, while using the results from the statistical model in the previous step to remove the confounding influence of natural spatial heterogeneity associated with gradients of climatic and edaphic variables. Third, we derive a spatial stochastic model using observed natural spatial heterogeneity, while also adding edge effects due to deforestation, and compare its output with data.
The data analysis shows that without the confounding effect of natural spatial heterogeneity, substantial bimodality is only observed for places close to agriculture. The model results indicate that the bimodality close to agricultural zones can be explained by anthropogenic edge effects due to logging and fire spread. Model results further show a sharp boundary between savanna and forest at the MP point, predictable from climate, soils and distance from human impact.
This shows that hysteresis is not required to reproduce bimodality. However, some limited remaining bimodality after accounting for natural and anthropogenic spatial heterogeneity indicates that there are regions of global bistability, although on smaller scales than previously recognized.
Many of the areas that have been savannas for a long time are colonized by humans. Restricting our analysis to pristine areas in deriving relations between natural variables and forest would then possibly lead to biased estimates of natural effects or an underestimation of hysteresis in the system, if present.
Therefore, we necessarily start from an estimate of pre-human forest cover that we take from the World Conservation Monitoring Centre WCMC original cover data set Comparing Fig.
The climatic data were obtained from the TRMM merged satellite-gauge rainfall data set 32 and the soil data from the harmonized world soil database The regression equation for the log odds of forest occurrence is.
See Supplementary Fig. The graphical representation of this equation is the surface in the space of predictor variables that best separates forest from nonforest, also called the decision boundary The coefficients in Table 1 can be seen as the components of a vector perpendicular to the decision boundary; the larger a particular component of that vector, the greater the influence of the corresponding predictor on occurrence of forest.
The largest effects are a positive effect of MAR, a negative effect of MSI and a mostly positive effect of soil clay fraction. Only when considering their combined effect Supplementary Fig. Further taking into account soils leads to a better prediction of the Atlantic Forest Supplementary Fig. This suggests that moist forests can only exist there due to favourable soil conditions. The interaction term of density and topsoil clay fraction implies that the effect of clay depends on density or the other way round.
A positive effect of clay fraction is consistent with previous empirical studies There is also a separate negative effect of bulk density that is consistent with the effects of higher soil compaction at higher densities. The more complicated effect of soils is most likely a consequence of the nonlinear relation between soil texture and soil hydrology.
Comparing the scatterplots with points sampled from areas close to human impact versus far from human impact will reveal how humans affect the dynamics. The spatial distribution of forest cover is shown in Fig. Figure 2b shows our subdivision of the study area in human impact classes. For this classification, publicly available satellite data 36 , 37 see Methods for details were utilized.
Figure 3 shows a plot of current tree cover sampled from natural areas versus MAR Fig. However, there exists a significant difference between the MSI colour scale of forest and savanna, indicating that at least some of the bimodality may be due to spatial heterogeneity associated with effects of seasonality that are independent from those of rainfall.
The scatterplot versus MSI Fig. This is also the case for the rainfall differences in the bimodality range of seasonality 0. To properly visualize the anthropogenic effects on bimodality, we need a measure that combines all predictor variables while minimizing the confounding effect of natural spatial heterogeneity. The best measure for this is the one that quantifies the distance perpendicular to the decision boundary. This is exactly what is done by the expression shown in equation 1.
As it is also a measure for the suitability of the natural environment for moist forest, we refer to it further as the climatic—edaphic forest suitability CEFS. Places with a large negative value are naturally unsuitable and places with a large positive value are suitable. If the human-unaffected system exhibits hysteresis on large scales, as suggested by previous work, we should see a wide interval of bimodality in a scatterplot of current tree cover versus CEFS where points are sampled from human-unaffected areas.
In Fig. The scatterplots also indicate via a colour scale the median of the tree cover change between and However, when looking at the areas distant from human influence Fig. For the transition areas Fig. Looking at the colour scale in Fig. This is presumably due to recovery from natural disturbance or a response to environmental change. Observed fire frequencies are lower at the very lowest and at the higher end of tree cover, with a hump in the middle Supplementary Fig.
This is consistent with the intermediate fire-productivity hypothesis Hence here, the lack of flammable material prevents fire occurrence or spread. At intermediate tree cover, in savannas, fires are not limited by fuel but by drought frequency. At high tree cover, droughts are less frequent, allowing the canopy to close at cost of the flammable grassy layer, resulting in lower fire frequencies.
We further produced a relative histogram of fire frequencies as a function of tree cover and annual water deficits to see how dryness affects fire frequency Supplementary Fig. The tree cover value below which fire occurrence becomes important increases steadily with dryness.
To explain these findings, we set up a stochastic partial differential equation model 43 , inspired by the ordinary differential equation model in ref. These external variables influence the dynamics by affecting growth and mortality rates in the equations. In modelling fire, we include both local fire and fire spread between pixels see Methods section for more detail.