When is serengeti migration 2011

This is captured by the simulated maps of Z in the shown in Fig. Conversely, the relatively dystrophic northern habitats of the Serengeti produce abundant low-quality biomass; while these areas are less nutritious during the wet season, they provide a refuge of last resort Fig.

The fragmentation of the landscape that is likely to result from road construction has the potential to gradually decouple the productive grasslands of the Serengeti plains from this dry-season refuge over time, and in the worst case, to dissect the system into separate habitats. In either event, the consequences of fragmentation are a loss of functional heterogeneity and a lowering of the carrying capacity of the system [15].

Similar conclusions have been reached about the ability of fragmenting African rangelands to sustain livestock numbers [20]. It should be acknowledged that a road might not by itself present an insurmountable barrier to migration, and therefore our model presents one possible scenario as far as the effects of the road on movement are concerned.

There are reasons to believe, however, that as road traffic increases, fences and development might follow, eventually rendering a simple road project into a de facto barrier [6]. Our model suggests that such a barrier would render the wildebeest population markedly more vulnerable to significant declines in its numbers, even without drought, and that such effects are magnified by droughts—which are inevitable in this system over any reasonable time horizon. Like all predictive models, the tool we present here inevitably has limitations.

For example, even though we allow for environmental stochasticity which introduces a substantial amount of uncertainty into our model output in our simulations, we lack precise estimates of process error mainly demographic stochasticity. We also still lack a specific mechanistic understanding of the importance of high-quality resources for birth rates in the plains during the wet season, and have had to infer the link between resource availability and population change through model fitting, as we have for example the effect of trees on grass biomass [14].

Our model, like others [21] , assumes that births are constant and that only mortality is resource-dependent. This is because higher-quality data are available to correlate dry-season mortality with rainfall than to infer the mechanistic basis of variation in birth rates [22].

The Serengeti wildebeest have a well-defined birthing season lasting a few weeks during the wet season [23] , and though markedly less variable than deaths, births have been observed to decline over time as the population has increased [22] , suggesting density-dependent regulation of birth rates. We assumed that only mortality is variable and resource-dependent, and also assumed that births occur year-round.

The second assumption alters the shape of the seasonal per capita population growth curves in Fig. Still, it is clear that identifying more clearly the role of grass biomass and quality on pregnancy and birth rates is critical for deriving more refined predictions of future population trajectories. It is unlikely that such refinements, however, would markedly alter our qualitative conclusions.

These and other caveats do add uncertainty to our predictions. As a counter-argument, the model assumes that wildebeest would instantaneously adjust to a more restricted landscape and seek to maximize resource acquisition without attempting to cross the road.

It predicts that as wildebeest in the South become deprived of the northern Serengeti and Mara habitats following barrier construction, they would automatically compensate for this loss by using more of the Western corridor, rather than aggregating at the now-truncated northern boundary of their altered range Fig.

It remains unclear how plastic the migratory behavior really is and to what extent the wildebeest may be actually able to adjust to dramatically new conditions i. Strong hard-wired components in behavior may govern important aspects of long-distance migratory movement, with local cues driving movement within the plains and woodlands, for example [14]. If this is the case, the simulation results would drastically underestimate the impact of cleaving the spatial integrity of the Serengeti into these two habitats by the proposed road.

We have also ignored the potential deleterious effects of other aspects of road construction, such as greater access for poachers [6] , an important consideration given the fact that the size of the Serengeti protected area substantially buffers it from poaching impacts at present [24] , [25].

These are all areas for further research and model improvement. In the meantime, the present model provides a rare quantitative tool for investigating the likely impact of disrupting the migration on the Serengeti wildebeest population. Given the iconic importance of the wildebeest migration, both for its tourism potential and ecological significance, we advocate further research on the potential consequences of habitat fragmentation.

Other models, both simpler [17] and more complex than ours such as the SAVANNA model [26] , [27] have been or could potentially be applied to this problem, and an ensemble modeling approach would potentially provide a more robust evaluation of the range of risks associated with road construction. For example, despite our overall prediction of population decline with barrier construction, our results are more conservative than were previous estimates generated by the simpler mean field model developed by Owen-Smith [17].

Part of the reason for this might be the ability of the southern subpopulation in our geographically-realistic model to access reasonably wet portions of the ecosystem south of the road during the dry season, as opposed to projections based on the simpler two-compartment Mara versus plains implementation of the earlier model [17]. Additional approaches could resolve these discrepancies — but it should be noted that all of our results suggest that the expected fragmentation resulting from road construction would not have strongly negative consequences for the keystone wildebeest population and thus much of the rest of the Serengeti ecosystem.

The Serengeti ecosystem extends over more than 30, km 2 in Tanzania and Kenya, with the Serengeti National Park as its dominant feature Fig. Here we define the ecosystem as the polygon defined by the extent of the wildebeest migration Fig. The migration is driven by two abiotic gradients: a seasonal rainfall gradient that increases from the Serengeti plains in the southeastern portion of the ecosystem towards the northwestern woodlands near Lake Victoria, and an opposing gradient of increasing soil fertility.

During the wet season, between December and April, the wildebeest seek high-protein grasses in the plains, but as the dry season progresses, they shift towards the wetter woodlands in search of remaining pockets of green but low-quality forage.

To investigate the effect of imposing movement constraints on wildebeest population dynamics, we used a recently-published model of savanna herbivore, vegetation, and fire dynamics, the SD model [14] , [24] , [28]. This is a discrete-time model that partitions the ecosystem into a spatially-realistic grid with a spatial resolution of 10 km, and tracks the dynamics of grass, wildebeest movement and population dynamics, fire, and tree dynamics in each lattice cell. Environmental stochasticity is introduced through the random generation of monthly rainfall surfaces.

The surfaces were generated by interpolating rain gauge data from the historical record for the period — To preserve intra-annual spatiotemporal correlations in the data, month runs spanning complete wet and dry season cycles were kept as a single unit. In the model, grass production and decay are functions of rainfall both seasonal and monthly and grazing intensity. Two components are tracked in each cell: green and dry grass. The protein content of the former is dictated by a separate layer of ecosystem-wide grass N content, developed from field data.

Wildebeest move at a weekly time step across the landscape, and their movements and local population growth are determined by a quantity we call Z , an index of resource availability. Previously, we used a model selection approach to derive the form for Z that best fit observed wildebeest movement data. A function of green forage intake I G and green forage protein content N provided the best fit: 1. Here, g is the proportion of each cell occupied by grass a function of tree cover, which for simplicity and to limit sources of uncertainty we keep constant in the present simulations and q is a parameter.

Emigrating wildebeest distribute themselves proportionately throughout the subset of target cells in the landscape with greater Z than the cell they have left. In our initial version of the model [28] , movement and local population dynamics were slightly decoupled.

In eq. This is a combination of local population dynamics the first term on the r. The implementation in eq. This required a recalibration of parameter a w from 0. This is the mean steady-state size of the Serengeti wildebeest population post-rinderpest when disease kept the population in check. We kept all other model parameters unaltered with respect to earlier model versions.

The full set of model equations and parameters is given in [28]. Both of these compartments contain mixtures of open grasslands mainly in the southern plains and woodland with variable amounts of tree cover. To simulate the presence of a barrier, we split the model lattice into a northern and southern compartment, with the size and shape of the compartments determined by the proposed road layout [6] Fig.

When no barrier is present, wildebeest are able to move freely across the entire landscape according to eq. To test for an effect of the barrier on wildebeest population size, we conducted model runs, each with randomly-drawn rainfall time series but with identical time series applied to the barrier and no barrier scenarios for each run , and calculated the percent deviation in final wildebeest population size between the two scenarios for the barrier scenario, the sum of the northern and southern sub-populations.

For both the default migration with no barrier and no migration scenarios, we conducted runs for years. To understand better the mechanistic basis of differences in population dynamics between the migration and no migration scenarios, we calculated the simulated per capita population change on a monthly basis, both across the entire lattice weighted by the relative abundance of wildebeest in each cell and in two lattice cells with high wildebeest abundance in the dry season a northern cell and in the wet season a southern cell.

This allowed us to compare the relative performance of the average resident and migratory wildebeest with resident wildebeest at the two extremes of the migratory range.

To examine uncertainty in model predictions as a function of uncertainty in the parameters, we conducted a global sensitivity and uncertainty analysis by drawing values for 20 model parameters from normal Gaussian or uniform distributions. In both cases, the means of the parameter distributions were centered on their default values Table 1. Standard deviations and ranges for the distributions were based on the literature and on the sampling distributions of parameters fit to data during model construction [28].

Many of the SD model parameters were derived in a hierarchical fashion by fitting model components e. We refit these distributions with the original model using maximum likelihood. We generated these by drawing parameter values from iterations following convergence of the Metropolis algorithm [14]. We then calculated standard deviations for the distributions in the Gaussian case and used these to sample parameter space in the sensitivity analysis with zero truncation for nonnegative parameters.

This approach is only an approximation and has potential drawbacks: for example, the correlation structure within sets of parameters e. These issues may under- or overestimate parameter uncertainty, but it was the best approach available given the assumptions built into our model e. Once we had constructed parameter error distributions, we ran iterations of the model using random deviates from these distributions, with all 20 parameters being sampled in each iteration.

We ran both the barrier and no barrier scenarios as before and calculated the absolute and relative drop in wildebeest numbers as a result of barrier construction at the end of years. We conducted both simple regressions of the response variables against each parameter with R v2. We derived a standard measure S of parameter influence: 5 where S i is the value of S for parameter i , and are the mean and standard deviation of parameter i , is the mean of the response variable absolute or relative change in wildebeest population size , Y p i is the value of Y at parameter value p i , obtained from the regression equation [24].

We would like to thank N. Thompson Hobbs and N. Owen-Smith for valuable comments on an earlier draft of the manuscript. Performed the experiments: RMH.

Analyzed the data: RMH. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract The Serengeti wildebeest migration is a rare and spectacular example of a once-common biological phenomenon. Introduction The Serengeti wildebeest migration is a unique part of our biological heritage.

Download: PPT. Figure 1. Map of the Serengeti ecosystem showing protected areas and geographic features. Results Model simulations predicted that the imposition of a barrier to migration at the site of the proposed road construction could plausibly cause significant drops in the wildebeest population. Figure 2. Simulated long-term effects of a barrier to migration across the northern Serengeti.

Figure 3. Simulated seasonal distributions of wildebeest and resources across the landscape. Figure 4. Simulated effects of movement on wildebeest population size in the Serengeti:. Table 1. Table 2. Distribution of values for the size of the wildebeest population with and without a barrier generated by the global sensitivity analysis. The sight of wildebeest crossing the crocodile-infested Mara River has been described as the seventh wonder of the world. Tanzania's Maasai battle game hunters for grazing land.

Serengeti road halt for wildlife. The annual migration of wildebeest attracts tourists from around the world. Related Topics. Kenya Tanzania. Published 18 April