Part 3

The Order of timing

The timing of entering logistic growth episodes for our native game, moose, mountain hare and roe deer, are described in Figure 2.4. Logistic growth rates coincide when availability of forage is plentiful in relation to population densities. The definition of logistic growth is when rates are accelerating up to a point where it reaches a maximum. This maximum is denoted by K/2 (see Fig. 2.3). When a population is exceeding that point, the growth rate will successively be reduced until it reaches a state of no change in population size (K). This process forms a S-shaped curve which can be fitted to logistic functions. In figure 2.4, only the roe deer tends to show that pattern.

The introduction above mentioned the importance of forage availability. The moose was the first game to enter a logistic phase, promoted by improving forage conditions initiated by re-vegetation of clear-cut areas. Its growth curve is smooth, indicating that the moose population never was close to or above carrying capacity since 1939. However, it seems that the moose population reached maximum growth rate (K/2) in 1979. The moose population never entered equilibrium density at actual carrying capacity. Management prematurely prevented further population growth as a measure to reduce damage to replanted Scot pine seedlings. At times, such browsing damage was particularly severe.

The mountain hare was the second species to enter a phase of accelerating growth, after having remained close to equilibrium with the actual carrying capacity since 1941. The roe deer was the last to enter logistic growth, 19 years after the moose and 5 years after the mountain hare.

The difference in timing of these growth phases is particularly interesting. Why was the moose first to respond? Why wasn’t the mountain hare first on the ball, despite already being present at fairly high densities? What delayed the roe deer before it entered its logistic phase? The answer is likely to be found in the dynamics of field-layer regeneration. It is important to recognize the difference in forest structure before and after modern forestry practices came into play.

In addition to browsing pressure, it is also important to consider the potential role of chemical vegetation control. During parts of this period—particularly from the 1950s to the early 1970s—herbicides such as hormoslyr were used in Swedish forestry to suppress deciduous vegetation in favor of conifer production. The spatial extent of these treatments was substantial, with approximately 1,000,000 hectares affected. Importantly, these applications were not limited to clear-cut areas, but also included active removal of regenerating deciduous tree vegetation through weeding practices, which likely reduced both current browse availability and future seed production. In areas where such treatments were applied, they would have directly reduced the abundance of herbaceous plants and deciduous browse, further limiting the recovery of the field layer.

The combined effect of intensive browsing and chemical suppression may therefore have amplified the reduction in forage availability, accelerating the transition from a productive post-clear-cut system to a more constrained and simplified vegetation structure.

To what extent could this suppression of available browse have intensified browsing pressure on replanted clear-cuts, thereby reinforcing the very damage that management aimed to prevent?

The forest stands changed markedly during the 1950s and 1960s. Prior to the widespread adoption of modern forestry, forests were more diverse and had lower stand densities, with uneven age distributions. From the 1950s onward, production forests became even-aged stands dominated by Scots pine and Norway spruce, resulting in higher stem densities and more uniform monocultures.

After clearing out complete forest stands, herbaceous and woody plants germinated and formed rapidly growing deciduous woody plants, such as birch, aspen, rowan, oak, and sallow. These plant species emerged from latent seed banks naturally present in the soil. Thus, the regeneration of field layer vegetation radically changed forage availability. In two to four years, woody plants formed dense stands, providing favorable moose forage. This led to easily accessible browsing for all age classes which promoted moose population growth and ovulation rates.

As mentioned above, this regeneration phase was in part counteracted by the use of herbicides, of which hormoslyr later became the most notorious. These treatments, applied not only on clear-cuts but also through active removal of regenerating deciduous vegetation, would have suppressed both the availability of browse and the longer-term contribution of deciduous species through reduced seed production.

The exponential phase in population growth rates for the moose started in 1966. Consequently, this led to an accelerating need for suitable forage as well. Eventually, this led to overbrowsing, resulting in dwarfed, shrubby woody plants, which possibly promoted mountain hare foraging. The mountain hare entered its logistic expansion 13 years later (1979). The roe deer needed another six years to enter its logistic phase. A valid guess is that the field layer vegetation was changing to a more herbaceous vegetation, which suited the roe deer but not the mountain hare. Possibly, this caused the late reaction that resulted in a threefold increase in roe deer harvest density.

The timings of population declines are also worth considering. Remember that the moose population was still on a steady increase when management actions ended the growth in 1982. In addition, the implied population density at peak harvest is highly sensitive to the assumed harvest rate. [added] A nominal culling rate of 30% would suggest unrealistically low population densities (~15 individuals per 1,000 ha). This indicates that the effective harvest rate was likely lower, implying that true population densities were substantially higher than suggested by nominal values. Therefore its peak in harvest densities does not reflect a point of equilibrium density. [end add]

Likewise, the undulating decline thereafter does not represent actual variation swings in carrying capacity. These swings are rather an effect of realized adaptive management. The mountain hare, only restricted by date limits when hunting was allowed, showed a short episode at density equilibrium. That more or less confirms that yearly bag sizes are based on encounter rates rather than preset numbers. The end of this short stay on equilibrium densities ceased in 1987. At that time the roe deer was in the beginning of its logistic growth. The extreme threefold increase in roe deer density coincided with a fast fivefold reduction in mountain hare harvest densities.

Summing up on timing

The main difference in forest habitats before and after the introduction of modern forestry is the diversity of tree species and age classes at lower spatial density that eventually was replaced by dense, even-aged monocultures of Scots pine and Norway spruce.

The former selection-based forestry brought more stable foraging conditions. This is supported by the rugged trends in mountain hare harvest densities during the early part of the timeline (1940s to 1966). The mountain hare culling has never been actively managed. Instead, culling success was based on encounter rates. As mentioned already, initially in this time window, the harvest densities of roe deer and moose were very low. After the initiative to restore the moose population, initiated by Svenska Jägareförbundet, both moose and roe deer entered an emerging recovery (see Fig. 2.1). At the same period, mountain hare showed a rugged general decline in harvest densities, while harvest densities for both moose and roe deer slowly but steadily improved. The decline suggests that the mountain hare experienced competition over forage - its carrying capacity was successively lowered by surging amounts of moose and roe deer. However, the observed decline may have other explanations as well. If the decline was an effect of forage competition, it suggests that parts of available browsing are shared by the game species. The early part of the timeline shows the prologue to what happened when the forestry switched to clear-cutting as a more rational method to manage the demand for timber and bulk. Such clear-cuttings opened not only to replantation of production forests. It also initiated germination of a latent seed bank of herbaceous and woody flora.

From 1966 to 1982 the moose population was growing logistically for 16 years. The maximum intrinsic rate (r) for the moose peaked in 1978, meaning that forage availability did not limit population reproduction capacity. At this point, the moose population was growing rapidly, increasing by 34% (λ = 1.34) - and so was the browsing pressure. After passing K/2 - the browsing pressure became larger than the regeneration capacity of the vegetation at clear-cutting precincts. This led to a more stressed and lower vegetation layer, probably suiting the reach considering the body size of the mountain hare.

Hence, the key process is the sequence of rapid vegetation change following clear-cutting. Initially, regeneration produces abundant and easily accessible forage, promoting rapid moose population growth. As browsing pressure increases, this vegetation is progressively suppressed, resulting in a lower and denser field layer. This transition likely created favorable foraging conditions for the mountain hare, which entered its logistic growth phase shortly after the moose reached maximum growth rate.

When suitable field layer forage became within reach for the mountain hare, it also entered a logistic growth phase just about when the moose was at maximum growth (K/2). The moose population was, however, still growing, but now with a waning growth rate. To sum it up, it took the moose 13 years from 1966 to reach maximum growth rate (K/2). At that time, the mountain hare entered the phase of logistic growth. This phase lasted only five years, without reaching a maximum growth rate. Instead it abruptly entered a short phase at equilibrium densities that lasted for three years, followed by a rapid decline, that ended when harvest density for the roe deer peaked in 1993.

The development of the roe deer population from 1966 to 2023 is harder to unravel. Like the mountain hare, the roe deer population has not been managed like the moose population. Nor has it been harvested fully in relation to encounter rates, but rather on anecdotal reasoning based on unorganized counts of observed specimens. Obviously, it seems that intensive moose browsing paved the way for the mountain hare. The roe deer, however, entered its fast and smooth logistic growth phase when the mountain hare was at equilibrium density. What makes the roe deer trace different from the mountain hare is the rugged trace of decline in harvest densities until it stabilized in 2010 (see Fig. 2.4).

Summary

• The far-reaching changes in forest habitats were, without doubt, caused by the profound modernization of forestry practices. This temporarily altered forage availability for native game species, supporting substantially higher population densities, as first-generation clear-cuts provided a richly diverse seed bank.

• Over time, replanting of clear-cuts with production species such as Norway spruce, combined with the removal of naturally regenerating deciduous vegetation, led to dense, even-aged stands. These changes resulted in more impoverished second-generation clear-cuts, negatively affecting subsequent forage availability.

• The observed timing largely reflects the first generation of clear-cuts combined with initially low browser densities. This led to rapid re-vegetation that primarily supported moose. Increasing moose densities in turn caused a rapid suppression of the shrub and bush layer, which eventually favored the mountain hare. Finally, when roe deer entered their logistic growth phase—while moose likely remained at too high densities—this coincided with the steep decline of the mountain hare.

• Although absolute population densities are uncertain, the timing of these shifts is robust and reflects real changes in population dynamics. This sequence explains the short delay in mountain hare response and the longer lag observed in roe deer.