Microtine rodents lemmings and voles

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There is no doubt that more effort has been expended overall in studying population cycles in microtine rodents (voles and lemmings) than in any other group of species. Cycle periods are typically 3 or 4 years, or much more rarely 2 or 5 years or even longer. These cyclic dynamics have been convincingly identified in a range of communities, including the following: voles (Microtus spp. and Clethrionomys spp.) in Fennoscandia (Finland, Norway and Sweden); lemmings (Lemmus lemmus) elsewhere in montane habitats in Fennoscandia; lemmings (Lemmus spp. and Dicrostonyx spp.) in the tundra of North America, Greenland and Siberia; voles (Clethrionomys rufocanus) in Hokkaido, northern Japan; common voles (Microtus arvalis) in central Europe; and field voles (Microtus agrestis) in northern England. On the other hand, there are many other microtine populations that show no evidence of multiannual cycles, including voles in southern Fennoscandia, southern England, elsewhere in Europe, and many locations in North America (Turchin & Hanski, 2001). It is also worth emphasizing that a quite different pattern, of irregular and spectacular irruptions in abundance and mass movement, is shown by just a few lemming populations, notably in Finnish Lapland. It is these whose suicidal behavior has been so grossly exaggerated (to say the least) in the name of film-makers' poetic license, unfairly condemning all lemmings to popular misconception (Henttonen & Kaikusalo, 1993).

Over many decades, the same range of extrinsic and intrinsic factors have been proposed to explain micro-tine cycles as have been directed at population cycles generally. Given the variety of species and habitats, it is perhaps especially unlikely in this case that there is a single all-encompassing explanation for all of the cycles. None the less, there are a number of features of the cycles that any explanation, or suite of explanations, must account for. First is the simple observation that some populations cycle while others do not. Also, there are cases (notably in Fennoscandia) where several coexisting species, often with apparently quite different ecologies, all cycle synchronously. And there are sometimes clear trends in cycle period, notably with increasing latitude (south to north) in Fennoscandia (see Section 14.5.1), where an explanation has been most intensively sought, but also for example in Hokkaido, Japan, where cyclicity increases broadly from southwest to northeast (Stenseth et al., 1996), and in central Europe, where cyclicity increases from north to south (Tkadlec & Stenseth, 2001).

A useful perspective from which to proceed is to acknowledge, as we have seen, that the rodent cycles are the result of a 'second-order' process (Bjornstad et al., 1995; Turchin & Hanski, 2001) (see Section 14.5.1); that is, they reflect the combined effects of a directly density-dependent process and a delayed density-dependent process. This immediately alerts us to the fact that, in principle at least, the direct and delayed processes need not be the same in every cyclic population: what is important is that two such processes act in conjunction.

We start with the 'intrinsic' theories. It is not surprising that voles and lemmings, which can achieve extremely high potential rates of population growth, should experience periods of overcrowding. Neither would it be surprising if overcrowding then produced changes in physiology and behavior. Mutual aggression (even fighting) might become more common and have consequences in the physiology, especially the hormonal balance, of the individuals. Individuals may grow larger, or mature later, under different circumstances. There might be increased pressure on some individuals to defend territories and on others to escape. Kin and non-kin might behave differently to one another when they are crowded. Powerful local forces of natural selection might be generated that favor particular genotypes (e.g. aggressors or escapists). These are responses that we easily recognize in crowded human societies, and ecologists have looked for the same phenomena when they try to explain the population behavior of rodents. All these effects have been found or claimed by rodent ecologists (e.g. Lidicker, 1975; Krebs, 1978; Gaines et al., 1979; Christian, 1980). But it remains an open question whether any of them plays a critical role in explaining the behavior of rodent populations in nature.

In the first place, we saw in Sections 6.6 and 6.7 the complexities of the relationships in rodents between density, dispersal, relatedness and ultimate survival and reproductive success. What is more, by no means all of this work has been carried out on species that exhibit cycles. Hence, there is little support for any universal rules, but there do seem to be tendencies many microtines cycle - and many don't trends in cyclicity cycles result from a second-order process dispersal, relatedness and aggression?

for most dispersal to be natal (soon after birth), for males to disperse more than females, for effective dispersal (arriving, rather than simply traveling hopefully) to be more likely at lower densities, and for fitness to be greater the greater the relatedness of neighbors. This has led some in the field to argue that the 'jury is still out' (Krebs, 2003), but others have simply doubted any role for these processes in the regulation of rodent populations, especially in view of the frequent inverse density dependence (Wolff, 2003). Certainly, while variations between individuals may be associated with different phases of the cycle, this is very far from saying that they are driving the cycles. If individuals disperse more at particular cycle phases, say, or are larger, then this is likely to be a response either to a present or to a past level of food or space availability, or to predation pressure or infection intensity. That is, intrinsic variations are more likely to explain the detailed nature of responses, whereas extrinsic factors are more likely to explain the causes of the responses.

None the less, in one case at least, an intrinsic cause has been proposed for the delayed density dependence. Inchausti and Ginzburg (1998) constructed a model with a 'maternal effect', in which mothers transmit their body condition phenotypically to their daughters, either from spring to fall or from fall to spring, and this in turn determines their per capita rate of growth. Thus, in this case the intrinsic quality of an individual is indeed a response to a past density, and hence to past resource availability, accounting for the delayed density dependence. Furthermore, when Inchausti and Ginzburg, focusing on Fennoscandia, fed what they believed to be reasonable values of population growth rate and the maternal effect into their model, both decreasing with latitude, they were able to recreate cycles with periods varying from 3 to 5 years (Figure 14.16). Turchin and Hanski (2001) criticized the parameter estimates (especially those of the growth rates) and claimed that the maternal effect model actually predicted 2-year cycles, at odds with those observed. Ergon et al. (2001) found with field voles, Microtus agrestis, from cyclic populations, that in transferring them between contrasting sites they rapidly took on characteristics appropriate to their new rather than their old populations - and certainly not those of their mothers. None the less, Inchausti and Ginzburg's results, set alongside the specialist predation hypothesis (see Section 14.5.1 and below), emphasize how the same pattern (here, the latitudinal gradient) might be achieved by quite different means. They also show that intrinsic theories remain 'in play' in the continuing search for an explanation for microtine cycles.

Turning now to extrinsic factors, there are two main candidates: predators and food. (Parasites and pathogens excited Elton's interest immediately after his original, 1924 paper, but they were largely ignored subsequently until recent technical advances made their study a serious possibility. It remains to be seen what role, if any, they

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Figure 14.16 Behavior of Inchausti and Ginzburg's (1998) maternal effect model with differing values of the maximum yearly reproductive rate, R, and the maternal effect, M, through which the quality of daughters in one season is affected by the quality of mothers in the previous season (fall or spring). The simulations are given 75 'years' to settle into a regular pattern. (a) R = 7.3; M = 15. (b) R = 4.4; M = 10. (c) R = 3.5; M = 5. (After Inchausti & Ginzburg, 1998.)

Time (years)

Figure 14.16 Behavior of Inchausti and Ginzburg's (1998) maternal effect model with differing values of the maximum yearly reproductive rate, R, and the maternal effect, M, through which the quality of daughters in one season is affected by the quality of mothers in the previous season (fall or spring). The simulations are given 75 'years' to settle into a regular pattern. (a) R = 7.3; M = 15. (b) R = 4.4; M = 10. (c) R = 3.5; M = 5. (After Inchausti & Ginzburg, 1998.)

play.) We have already made a start in examining predators in Sections 10.4.4 and 14.5.1. Their importance in microtine cycles, expressed as the 'specialist predation hypothesis', has received considerable support since around 1990 from a series of mathematical models and field experiments, especially from workers focused on the cycles in Fennoscandia. The hypothesis, put simply, is that specialist predators are responsible for the delayed density dependence, whereas generalist predators, whose importance varies with latitude, are a major source of direct density dependence.

Early field experiments in which predators were removed (in Fenno-scandia and elsewhere), although they maternal effects?

the specialist predation hypothesis experimental support?

typically led to 2-3-fold increases in vole density, were subject to various criticisms of their experimental design: they were short term, or small scale, or they affected too many, or too few, of the predator species, and they often involved the erection of protective fences that are likely to have affected movements of the prey (voles) as well (Hanski et al., 2001). Conclusive experiments may be a necessity, but this does not make them any easier! More recent experiments, too, give rise to some of the same misgivings. Klemola et al. (2000) excluded all the predators from four fenced (and net-roofed) exclosures in western Finland, 1 ha in size, for 2 years. The vole populations in the exclosures increased more than 20-fold in abundance compared to the control grids, until food shortages caused them to crash (Figure 14.17a). But the effects of specialists and generalists were inevitably combined in such a design; and while results such as these indicate an important role for predators in vole survival and abundance, they cannot prove

Figure 14.17 (a) Mean abundances of voles (±SE) from four small predator-exclosure grids (•) and four control grids (o) in western Finland. (After Klemola et al., 2000.) (b) The density of voles (mean number of individuals caught per trapline, ±SE, in April, June, August and October) from four large predator-reduction sites (•) and four control sites (o) in western Finland. Predator reduction occurred only over the summer and vole densities tended to revert to control levels over the winter. (After Korpimaki et al., 2002.)

a role in causing (as opposed, say, to amplifying) the vole cycles. Korpimaki et al. (2002), worked in the same area but used four much larger unfenced areas (2.5-3 km2) for 3 years, reducing predator abundance over the summer but not the winter: mustelids (stoats and weasels) by trapping, and avian predators by removing natural and artificial nesting sites. Predator reduction increased vole density fourfold in the first (low) year; it accelerated an increase in density twofold in the second year; and it increased fall density twofold in the third (peak) year (Figure 14.17b). But again, specialists and generalists were not distinguished, and the temporal pattern of abundance was essentially unaltered.

The specialist predation model itself, which has been successively refined in a series of studies (the refinements are traced by Hanski et al., 2001) has the following key features: (i) logistic population growth in the microtine prey, to reflect the directly density-dependent effects of food shortage on the microtines, preventing their populations from growing too large before specialist predators 'catch up'; (ii) specialist predators (weasels) with a population growth rate that declines as the ratio of specialist predators to prey increases; (iii) seasonal differences in the breeding of voles and weasels in the summer and winter; and (iv) generalist predators - generalist, switching mammals or wide ranging (nomadic) avian specialists that act in a directly density-dependent manner by responding immediately to changes in microtine density. Note, therefore, that the model includes both of the most-studied extrinsic factors: predators and food. Food provides the baseline direct density dependence; specialist predators provide the delayed density dependence. The generalist predators then provide a further source of direct density dependence that can be varied to mimic their known decline in abundance with latitude.

When the model is parameterized with field data from Fennoscandia, it can recreate an impressive number of the features of the observed dynamics. Cycles are of broadly the correct amplitude and period, and both the period and indeed the amplitude of the cycles increase with latitude as the density of generalist predators decreases, as observed in nature (Figure 14.18). A related model for the collared lemming, Dicrostonyx groen-landicus, preyed upon by one specialist predator (the stoat, Mustela erminea) and three generalists (Gilg et al., 2003) was also able to recreate observed cycles in Greenland when parameterized with field data.

On the other hand, not all studies have conformed to the predictions of the specialist predation model. Lambin et al. (2000) described regular cycles of field voles in Kielder forest, northern England (55°N), with a period of 3-4 years and an approximately 10-fold difference between peak and trough densities (a difference of 1 on a log scale, such as on Figure 14.18). Yet, parameterizing the specialist predation model with the estimated intensity of generalist predation at this site would have predicted no cycles whatsoever - as would the latitude. What is more, a rigorous

Lemmings Regular Cycle

Figure 14.17 (a) Mean abundances of voles (±SE) from four small predator-exclosure grids (•) and four control grids (o) in western Finland. (After Klemola et al., 2000.) (b) The density of voles (mean number of individuals caught per trapline, ±SE, in April, June, August and October) from four large predator-reduction sites (•) and four control sites (o) in western Finland. Predator reduction occurred only over the summer and vole densities tended to revert to control levels over the winter. (After Korpimaki et al., 2002.)

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Figure 14.18 (a) Sample data generated by the specialist predation model, and the associated autocorrelation functions (ACFs), for various values of generalist predator abundance, G. As G increases, cycle period increases and cycle amplitude decreases, and at sufficiently high values the dynamics are sufficiently stabilized for the cycles to disappear altogether. (b) Comparable time series from five field sites: Kilpisjarvi (69°N; period = 5), Sotkamo (64°N; period = 4), Ruotsala (63°N; period = 3), Zvenigorod (57°N; period = 3) and Wytham Wood (51°N; no significant periodicity). (After Turchin & Hanski, 1997.)

program of reducing weasel numbers (i.e. specialist predators) at unfenced grids within the site (by about 60% in comparison with control sites) increased adult vole survival by about 25% but had no appreciable impact on the cyclic dynamics (Graham & Lambin, 2002).

Lambin and his collaborators conclude from these studies that generalist predators may not, after all, be responsible for the Fennoscandian gradient in cycle length; and that vole cycles need not be the result of the impact of specialist (i.e. weasel)

predation (since they seem not to be at Kielder). Remember, too, that the results of time series analyses (see Section 14.5.1) and predator removal studies in Fennoscandia are consistent with the specialist predation hypothesis but do not prove it. In contrast, the response to these results of adherents of the specialist predation hypothesis (e.g. Korpimaki et al., 2003) has been to emphasize that the Kielder cycles are different from those in northern Fennoscandia (lower amplitude at Kielder, higher trough densities, less spatial synchrony and only one vole species involved). That is, they contend that the results at Kielder may tell us little or nothing about cycles in Fennoscandia. Though we may strive to avoid it, even rigorous studies are often open to alternative interpretations.

Turning finally to the role of food, both field observations and experiments suggest that it would be unwise to assume that the same forces act on voles and lemmings (Turchin & Batzli, 2001). In the first place, voles typically eat a range of vascular plants, including graminoids (grasses and sedges), whereas lemmings feed on a mixture of mosses and graminoids. Voles seem rarely to consume more than a few percent of available plant material (though, of course, the quality of the available food might be more important than its quantity -see, for example, Batzli, 1983); and food supplementation has typically failed to increase vole abundance (though experiments may have been foiled by the 'pantry effect' through which predators are attracted to high vole densities, counteracting the effects of supplementation). Lemmings, on the other hand, at peak densities, typically remove more than 50% and sometimes as much as 90-100% of the available vegetation.

Furthermore, through an analysis of models, Turchin and Batzli (2001) show clearly that the role that vegetation might play in cyclic dynamics depends critically on the nature of the vegetation itself, especially the vegetation's dynamics following significant consumption by herbivores. If the dynamics are logistic (i.e. S-shaped) then this may provide the delayed density dependence necessary to generate 'second-order' cycles in microtine abundance. But if the dynamics are 'regrowth' (i.e. a rapid initial response, decelerating until a saturation abundance is reached) then any density dependence will be direct rather than delayed. In this case, the microtine-food interaction may play an integral part in cyclic dynamics (as they do, for example, in the specialist predation hypothesis), but it cannot be the second-order driving force. Crucially, plants consumed by voles seem likely to exhibit rapid regrowth dynamics because of the large proportion of unconsumed plant parts, much of it underground. By contrast, mosses are, by their nature, wholly available to their consumers, and when lemmings devastate their vegetation they often grub underground for graminoid rhizomes and destroy these too. Lemming vegetation, therefore, is likely to exhibit logistic dynamics: rapid only after a slow start.

On this basis, Turchin and Batzli parameterized a model for microtines and a food supply with logistic growth, using what data were available for the brown lemming (Lemmus sibiricus) and its vegetation at Barrow, Alaska (Batzli, 1993). The results were encouraging although not perfect representations of the observed patterns: cycle amplitudes were too low (400- rather than 600-fold) and too long (6 rather than 4 years). On the other hand, uncertainty, and in some cases plain ignorance, surrounded several of the parameter estimates. The model can be 'tweaked' to generate the observed dynamics. Further painstaking work in the field, especially to obtain winter parameter estimates from under the snow, will be required to determine whether such tweaking is justified by the truth about lemming biology.

The cycles of microtines have been studied for longer and with greater intensity than those of any other species, and have generated more theories to explain them, and more disagreements amongst disputing advocates. At the time of writing, a near-consensus appears to have been arrived at that a conjunction of direct and delayed density dependence is required to account for observed patterns; and most support is attracted to the contention that specialist predators provide the delayed density dependence, while food shortage and generalist predators provide the direct density dependence. All scientific 'conclusions' are provisional, however, and fashions change in science as in everything else. It remains to be seen how robust and universal currently fashionable explanations prove to be.

More generally, we started this chapter with a series of questions. Why are in conclusion some species rare and others common?

Why does a species occur at low population densities in some places and at high densities in others? What factors cause fluctuations in a species' abundance? Having reached the end of the chapter, it should be clear that none of these questions has a simple answer. We have seen for particular examples why a species is rare, or why another varies in abundance from place to place. But we must not expect the answer to be the same for every species - especially when we start a new study of a species that demands our attention, perhaps by its excessive abundance (a pest) or declining abundance (a target for conservation). It is crucial none the less that we have a clear idea of what the possible answers might be and how we might go about obtaining those answers. The aim of this chapter has been to examine those possibilities and how to distinguish them. In the next chapter, we turn explicitly to some of the pressing examples of populations whose abundance we need to understand in order to exert some measure of control - be they pests or natural resources that we wish to exploit.

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  • Laura Frankfurter
    Why large microtine populations in north?
    7 years ago

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