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Research Article

Disturbance effects on timberland returns

Abstract

Probability theory is applied to the effect of severe disturbances on the return rate on capital within multiannual stands growing crops. Two management regimes are discussed, rotations of even-aged plants on the one hand, and uneven-aged semi-stationary state on the other. The effect of any disturbance appears two-fold, contributing to both earnings and capitalization. Results are illustrated using data from a recently published study, regarding spruce (Picea abies) forests in Austria. The economic results differ from those of the paper where the data is presented, here indicating continuous-cover forestry is financially inferior to rotation forestry. Any severe disturbance may induce a regime shift from continuous-cover to even-aged forestry. If such a regime shift is not accepted, the disturbance losses reduce profits but do not affect capitalization, making continuous-cover forestry financially more sensitive to disturbances. Revenue from carbon rent favors the management regime with higher carbon stock. The methods introduced in this paper can be applied to any dataset, regardless of location and crop species.

Author summary

A method is developed for the evaluation of the financial effects of disturbances of periodic growth processes. It is found that not only profit rate, but also capitalization is affected. In the case of a non-periodic growth process, however, profit rate is affected but not necessarily capitalization. This makes the periodic growth process financially more robust to disturbances.

Citation: Kärenlampi PP (2024) Disturbance effects on timberland returns. PLOS Sustain Transform 3(12): e0000146. https://doi.org/10.1371/journal.pstr.0000146

Editor: Isabel Marques, University of Lisbon: Universidade de Lisboa, PORTUGAL

Received: January 28, 2024; Accepted: October 23, 2024; Published: December 11, 2024

Copyright: © 2024 Petri P. Kärenlampi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Example data in this paper was used as described in the text. The example case appears at https://link.springer.com/article/10.1007/s10640-022-00719-5. The example case data was the only data used.

Funding: This study was partially financed by Niemi Foundation. PPK received the funding. No exact amount can be allocated to this study. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Recently, a combination of drought and bark beetles has induced large salvage cuttings of forests in Central Europe [1,2,3]. Combinations of windfall and bark beetles have been devastating [4,5]. Extended drought also has induced an increased risk of wildfire [6,7,8]. Severe disturbances, probably of largely anthropogenic origin, have increased [9,10,11]. It appears that the increased disturbances are threatening the sustainability of forest ecosystems, as well as that of forest-based industries and economies [12,13,14,15]. Management procedures increasing the resilience of forest systems are urgently needed.

Severe disturbances on forest stands are often induced by an interplay of factors like drought, fire, wind, snow, landslides, floods, volcano eruptions, insects, pathogens, and sometimes mammals [16,17,18,19,20,21,22,23]. A European report indicates insect damage and wildfires have diminished from year 2000 to 2015, whereas snow and wind damage have increased [24,25]. Not all large-scale disturbances need to be stand-replacing [26,27]. Wind damage rates may significantly differ between decades [28]. Some authors consider mortality events removing several percentages of trunk volume as catastrophic, appearing as an interplay with background mortality [29,30]. Within the boreal region, the dominant stand-replacing disturbance is fire, with a natural return interval of decades or centuries [27,31,32,33,34, cf. 35]. The importance of windthrow appears to increase towards the temperate zone, often interacting with bark beetles [5,10,36,37,38]. Prolonged interval between stand-replacing disturbances appears to lead to gap regeneration [26,27,39,40,41,42,43,44,45]. Different mechanisms tend to control gap initiation and gap expansion [39].

The recovery rate of a system from any disturbance naturally depends on the kind of disturbance. Losses of growing stock in the context of forest stand thinning (a non-stand-replacing disturbance) are the fastest recovered in the case of young stands with relatively low density, also affected by precipitation and temperature [46].

Another question is financial performance in the occurrence of disturbances. Several investigations discuss mortality of forest trees in terms of stem counts [47]. Neglecting the size of dying trees however impairs the applicability of such studies, from the viewpoints of stand value and stand productive capacity, as mortality of individual trees due to within-species competition concentrates in small stems, as well as trees with slow growth rate [21,27,48,49,50,51,52,53,54,55,56,57,58,59,60]. However, tree species differ with respect to factors dominating mortality [60,61,62,63,64,65]. With increasing tree size, physical mortality drivers appear to increase with the expense of biotic reasons [42,66]. Dying trees may or may not remain standing, and their positions may be clustered [44,67]. Aging of stands appears to increase sensitivity to disturbances [68,69,70,71,72,73,74]. Young forests and small trees however appear more sensitive to heat and drought than old forests and large trees [75,76,77]. On the other hand, adverse weather conditions may have long-term effects, possibly interacting with pathogens, insects, and competition [19,52,55,78,79,80,81,82,83]. Disintegration of functional processes has been proposed as a causal explanation [84]. Hot draughts are more lethal than temperate ones [85]. Some observations indicate low temperatures induce mortality [77]. Relationships between forest stand observables and demographic processes change along with time, possibly due to changing climate [10,12,13,14,15,17,18,79].

A severe, unintended disturbance may or may not require cultivation with new seedlings. If it does require cultivation in the case of continuous-cover forestry, at least a temporary regime shift into even-aged forestry takes place. In even-aged rotation forestry, regenerating (or stand-replacing) disturbances do not induce any regime shift. However, the crop may be lost, completely or partially.

The time needed to recover from a regenerating disturbance can be addressed as follows. Within rotation forestry, the expected value of recovery time from a realized regenerating disturbance is (1) where k(a) is the probability density of realizing regenerating disturbances within the rotation time τ. In the possible case of evenly distributed regenerating disturbances, the expected value of the recovery time would approach half of the rotation time–other kinds of distributions also are feasible [68,69,2].

Fig 5A of Knoke et al. [86] indicates that after a regenerating disturbance, the recovery time into the semi-stationary state of continuous-cover forestry is 60 years at least, corresponding to one full rotation within the even-aged management.

Financial performance indeed is another subject. Fig 5A of Knoke et al. [86] indicates that the financial performance of the continuous-cover forestry impairs as the semi-stationary state is approached. Correspondingly, the recovery time does not have any direct relation to financial performance, which is here approached in terms of return rate on capital.

The economic value of a forest is given by Knoke et al. [86] as (2) where is the time rate of profits, r is a discount rate, and b is the present time. Such an estimate of the economic value strongly depends on the discount rate. Another complication is that the profit rate varies not only with time, but also with a variety of factors, including forest age. The latter complication can be overcome by integrating the profit rate over stand age distribution, to produce an expected value (3) where a is stand age, p(a) is its probability density, and τ is rotation age.

At this stage the discount rate r is unknown. However, in developed markets, the forest value C is known as a market value. Then, the expected value of the discount rate can be readily resolved from Eq (2), (4) where it gains the interpretation of the expected value of the return rate on capital at time b. Such an expected capital return rate may apply to a single stand by the collapse of the probability density functions. On the other hand, being determined on the basis of the market values of forest estates, it can be taken as the capita return rate requirement on the market.

In the remaining part of this paper, we will develop produces for discussing the effects of stand-replacing disturbances on the expected capital return rates in terms of probability theory. The question is nontrivial since disturbances contribute to both profit rate (Eq (3)) and to capitalization (Eq (2)). The procedures to be developed are supposed to be universal–results however will be produced regarding two management systems of spruce forests in Austria, using data from the recent publication from Knoke et al. [86]. The two management regimes discussed here are

  • even-aged forestry with artificial regeneration
  • continuous-cover forestry with uneven-aged structure.

It is worth noting that the continuous-cover forestry discussed here differs from the system of prolonged rotations with partial artificial and partial natural regeneration discussed by Knoke et al. [86].

This paper focuses on financial considerations. Carbon sequestration will be discussed from the financial viewpoint. Financial aspects of biodiversity, as well as those of the social values of forestry, appear to be undeveloped [87,88,89,90], and will not be discussed in detail.

Materials and methods

In this paper, the simplest possible treatment is attempted, provided the relevant features of the system studied are not jeopardized. Correspondingly, numerical treatments rely on a constant probability density of stand age within the rotation age in undisturbed systems [91,92,93]. As there is no convincing data suggesting otherwise [68,69,70,71], the time rate of regenerating disturbance density is not modeled to vary with respect to stand age. Fungal damage tends to be greater in continuous-cover than in even-aged forestry, but the effect depends on tree species [94,95]. The time rate of regenerating disturbances is here not modeled as a function of the management regime but is used as an input parameter for any regime.

Not all disturbances are regenerating. Forest stands may suffer value losses even if not regenerated. On the other hand, stands facing regenerating disturbances do not necessarily suffer total damage–some part of the stand value may be salvaged. This study will resist the temptation to discuss any spectrum of damage severity. Instead, simplicity is aspired by compensating the value loss of non-regenerated damaged stands by the eventual salvage value of stands to be regenerated. Correspondingly, neither the partial damage nor the salvage value will explicitly appear below.

As discussed above, a regenerating disturbance may or may induce a regime shift from continuous-cover forestry to even-aged forestry. Aspiring simplicity, this paper does not discuss such a regime shift. Instead, the effect of regenerating disturbances on the profit rate and capitalization in continuous-cover forestry is treated as discussed below.

Regenerating disturbances inducing the loss of crops have financial implications. The probability density of stand age appearing in Eq (3) certainly is affected, and it affects not only the profit rate, but also the capitalization. The expected value of capitalization appearing in Eq (4) can naturally be written (5)

Then, provided p(a) is taken as the probability density of stand age in the absence of regenerating disturbances, the probability density in the presence of disturbances becomes (6) where is the rate of regenerating disturbance density, is the probability of stand survival to age a, and is the probability density normalization factor. The normalization factor becomes like this provided the non-disturbed probability density of stand age p(a) is taken as a constant, corresponding to evenly distributed stand ages. The effect of disturbances can be straightforwardly addressed by substituting Eq (6) into Eqs (3) and (5), and further to Eq (4).

A question arises whether the above Equations are applicable to continuous-cover forestry. Eqs (2), (3), (4), and (5) indeed are applicable, but the rotation time τ as a parameter deserves some discussion. In semi-stationary continuous-cover forestry, an operative rotation time corresponds harvesting rotation, rather than the life cycle of trees. Eqs (1) and (6) discuss regenerating disturbances. In the case of continuous-cover forestry, any regenerating disturbance induces a temporary shift of management regime, at least if regeneration is aided in terms of cultivation.

It is, however, possible to apply the rate of regenerating disturbance density to continuous cover forestry, assuming that the disturbance is distributed and does not become compensated by cultivation. One can then assume that the disturbance loss rate is deducted from the profit rate, resulting as disturbance-affected profit rate (7)

It is here assumed that provided , any disturbance only reduces the profit rate, and does not affect the capitalization within the continuous-cover regime.

Let us then turn to the recent publication of Knoke et al. [86] for the clarification of input variables characteristic for spruce forest in Austria. Firstly, Table 2 of [86] indicates a regeneration expense of 2000 Eur/ha. Fig 5 of [86] indicates a bare land value of 10000 Eur/ha, and the value of trees of 18000 Eur/ha at the age of 60 years. Within this range, the Figure implies an expected value of capitalization of 21000 Eur/ha. Regarding the continuous-cover forestry, Fig 5A of Knoke et al. [86] indicates a semi-stationary state capitalization of 24000 Eur/ha. Table 2 of Knoke et al. [86] indicates a profit rate of 154 Eur/(ha*a) at the semi-stationary state.

The publication of Knoke et al. [86] does not indicate the volumetric timber stocks, or stored amounts of CO2. Given the 3000 Eur/ha difference in the expected value of the capitalization among the management regimes, one can approximate a timber stock difference in the order of 50 m3/ha.

Within the regime of even-aged rotation forestry, the expected value of the profit rate is naturally produced using Eq (3), and that of capitalization according to Eq (5).

Results

Fig 1 shows the expected value of capital return rate [Eq (4)] as a function of disturbance density rate for the two regimes. In the absence of disturbances the capital return rate of even-aged rotation forestry is two times that of the continuous-cover forestry. The continuous-cover forestry is much more sensitive to disturbances, as any expected annual damage loss directly reduces profit according to Eq (7): within the range of disturbance densities appearing in Fig 1, the disturbances reduce the expected profit up to 86%, and the capital return rate correspondingly. The effect of disturbances on the expected value of capital return rate within the even-aged rotation forestry is much gentler, diminishing only 20% in Fig 1.

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Fig 1. The effect of disturbances on the expected value of capital return rate for the two regimes.

https://doi.org/10.1371/journal.pstr.0000146.g001

As the effect of disturbances on the finances of the continuous-cover forestry is rather simple according to Eq (7), the even-aged rotation forestry case is a more delicate subject of discussion, containing a change in the profit rate, change in capitalization, change in survival probability of individual stands, as well as probability density of stand age.

Fig 2 shows that the capitalization decreases slightly, according to Eq (5). The capital return rate reduces at most 18%, and the profit rate at most 20%. The difference between the change in the capital return rate and the profit rate is due to the change in capitalization, according to Eq (4).

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Fig 2. The effect of disturbances on capitalization, capital return rate, profit rate, and survival probability in even-aged rotation forestry.

https://doi.org/10.1371/journal.pstr.0000146.g002

Interestingly, the biggest change due to disturbances is in the survival probability of stands until final harvesting, at most -28% (Fig 2). Considering that positive revenue is gained only from final harvesting (cf. Fig 5 of [86]) one might ask why the profit rate is not as much affected as the stand survival probability. The answer naturally lies in the probability density of stand age.

In Fig 3, the appearance density of mature stands is reduced by 16%, even if the survival probability of individual stands is reduced by 28%. The reason for this difference is that the probability density of newly established stands increases by 17% along with disturbations. Correspondingly, there is a greater number of stands regenerated annually. The probability density of mature stands can be verified as the product of that of newly regenerated stands, multiplied by the survival probability.

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Fig 3. The effect of disturbances on the change in appearance density of newly regenerated stands and stands mature for harvesting.

https://doi.org/10.1371/journal.pstr.0000146.g003

An issue worth discussing is the applicability of the two management regimes in carbon sequestration and storage [96]. Within the two implementations described by Knoke et al. [86], the semi-stationary state in the continuous-cover forestry has greater capitalization and greater timber stock, in relation to the expected values within rotation forestry. The greater timber (or carbon) stock may become compensated in terms of a carbon rent. An additional carbon stock of 50 tons/ha, compensated with a carbon rent of 2 Eur/(ton*a), would increase annual revenue within the CCF by 100 Eur/ha. Such additional revenue would improve the capital return rate within CCF, and Fig 1 would become modified as shown in Fig 4. It is found that the capital return rate within the CCF still is inferior to that of RF, and it still is more sensitive to disturbances. Obviously, an increment of the carbon stock could be aspired within the rotation forestry. Details of such an attempt however are beyond the scope of this study.

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Fig 4. The effect of disturbances on the expected value of capital return rate for the two regimes, when the greater carbon stock of the CCF is compensated by a carbon rent of 100 Eur/(ha*a).

https://doi.org/10.1371/journal.pstr.0000146.g004

Discussion

The present treatment is extremely simplified. First, one might ask whether it induces any bias that the non-disturbed probability density of stand age for undisturbed forest p(a) was taken as a constant. Strictly, the constancy would refer to an assumption of a normal forest–structure [91]. Such an assumption however is not necessary; any single stand can be observed at a random time instant within any rotation, corresponding to a constant probability density of stand age [92,93]. On the other hand, the procedures above allow any analyst to repeat the procedure with appropriate probability density of stand ages.

Secondly, one might ask what are the consequences of assuming a constant regenerating disturbance density rate with respect to stand age. Aging is known to make forests more vulnerable [68,69,70,71]. However, the rotation ages applied here are probably not long enough for such effects to materialize. Again, any analyst is invited to insert desired age-dependent disturbance rates.

A third question is the definition of the “regenerating disturbance”. Intimately related to this is any eventual revenue from salvage harvesting. In Eq (6), a regenerating disturbance nullifies the local stand age and induces a regeneration expense. Any salvage revenue is omitted. In reality, there often is salvage revenue. On the other hand, there are disturbances not resulting in regeneration. The justification for simply omitting any salvage revenue is that such a procedure compensates the omitted loss due to non-regenerating disturbances. The same goes with continuous-cover forestry: omitted salvage revenues compensate any non-salvaged non-regenerating damage.

In general, case studies are not guaranteed to be representative of any population. There is no guarantee that the data used in this study would be representative of spruce forests in Austria. It is questionable whether the cross profits, as well as capitalizations appearing in the semi-stationary state of continuous-cover forestry are sustainable [97,98]. One must realize that the data used here describes one example case. The methods used above can be readily applied to any available set of cross profits and capitalizations, corresponding to different management regimes.

The outcome regarding financial resilience in the occurrence of disturbances is very different from that of Knoke et al. [86]. An obvious reason is that the present treatment is purely financial, whereas Knoke et al. [86] applied a mixture of economical and time-delay criteria. Another major difference is that we have here discussed continuous-cover forestry as one management regime, instead of prolonged rotations with partially artificial and partially natural regeneration.

Conclusions

Procedures were developed for the assessment of financial consequences of severe disturbances on multiannual stands growing crops, in terms of probability theory. Two management regimes were discussed, rotations of even-aged plants on the one hand, and uneven-aged semi-stationary state on the other. The effect of any disturbance appeared two-fold, contributing to both earnings and capitalization. Results were illustrated using data from a recently published study, regarding spruce (Picea abies) forests in Austria. The economic results differed from those of the paper where the data was presented, here indicating continuous-cover forestry is financially inferior to rotation forestry. Any severe disturbance may induce a regime shift from continuous-cover to even-aged forestry. If such a regime shift is not accepted, the disturbance losses reduce profits but do not affect capitalization, making continuous-cover forestry financially more sensitive to disturbances. Revenue in terms of a carbon rent favors the management regime with higher carbon stock. The methods introduced in this paper can be applied to any dataset, regardless of location and tree species.

References

  1. 1. Jakoby O, Lischke H, Wermelinger B (2019) Climate change alters elevational phenology patterns of the European spruce bark beetle (Ips typographus). Global Change Biology 25:4048–4063 pmid:31310430
  2. 2. Netherer S, Panassiti B, Pennerstorfer J, Matthews B (2019) Acute Drought Is an Important Driver of Bark Beetle Infestation in Austrian Norway Spruce Stands. Frontiers in Forests and Global Change 2.
  3. 3. Lindner M, Verkerk H (2022) How has climate change affected EU forests and what might happen next? European Forest Institute, https://efi.int/forestquestions/q4, accessed November 21, 2022.
    • 4. Thom D, Seidl R, Steyrer G, Krehan H, Formayer H (2013) Slow and fast drivers of the natural disturbance regime in Central European forest ecosystems, Forest Ecology and Management 307:293–302 https://doi.org/10.1016/j.foreco.2013.07.017.
    • 5. Økland B, Nikolov C, Krokene P, Vakula J (2016) Transition from windfall- to patch-driven outbreak dynamics of the spruce bark beetle Ips typographus, Forest Ecology and Management 363:63–73 https://doi.org/10.1016/j.foreco.2015.12.007.
    • 6. Fernandes PM (2019) Variation in the Canadian Fire Weather Index Thresholds for Increasingly Larger Fires in Portugal. Forests 10:838.
    • 7. Moreira F, Ascoli D, Safford H, Adams MA, Moren JM, Pereira JMC et al (2020) Wildfire management in Mediterranean-type regions: paradigm change needed. Environmental Research Letters 15 011001.
    • 8. San-Miguel-Ayanz J, Moreno JM, Camia A (2013) Analysis of large fires in European Mediterranean landscapes: Lessons learned and perspectives. Forest Ecology and Management 294:11–22
    • 9. Forzieri G., Girardello M., Ceccherini G., Spinoni J., Feyen L., Hartmann H. et al (2021) Emergent vulnerability to climate-driven disturbances in European forests. Nature Communications 2021, 12, 1081. 366 pmid:33623030
    • 10. Seidl R, Schelhaas M-J, Rammer W, Verkerk PJ (2014) Increasing forest disturbances in Europe and their impact on carbon storage. Nature Clim. Change 4:806–810 pmid:25737744
    • 11. Seidl R, Schelhaas M-J & Lexer MJ (2011) Unraveling the drivers of intensifying forest disturbance regimes in Europe. Global Change Biol. 17: 2842–2852.
    • 12. Clark JS, Bell DM, Hersh MH, Nichols L (2011) Climate change vulnerability of forest biodiversity: Climate and competition tracking of demographic rates. Global Change Biology 17:1834–1849
    • 13. Seidl R, Thom D, Kautz M, Martin-Benito D, Peltoniemi M, Vacchiano G et al (2017) Forest disturbances under climate change. Nature Climate Change 7:395–402 pmid:28861124
    • 14. Etzold S, Ziemińska K, Rohner B, Bottero A, Bose AK, Ruehr NK et al (2019) One century of forest monitoring data in Switzerland reveals species-and site-specific trends of climate-induced tree mortality. Frontiers in Plant Science 10:307 pmid:30967884
    • 15. Astigarraga J, Andivia E, Zavala MA, Gazol A, Cruz-Alonso V (2020) Evidence of non-stationary relationships between climate and forest responses: Increased sensitivity to climate change in Iberian forests. Global Change Biology 26:5063–5076. pmid:32479675
    • 16. Turner MG, Dale VH (1998) Comparing large infrequent disturbances: what have we learned? Ecosystems 1:493–496
    • 17. Williams AP, Allen CD, Millar CI, Swetnam TW, Michaelsen J, Still CJ et al (2010) Forest response to increasing aridity and warmth in the southwestern United States. Proc. Natl. Acad. Sci. 107:21289–21294
    • 18. Cansler CA, McKenzie D (2014) Climate, fire size, and biophysical setting control fire severity and spatial pattern in the northern Cascade Range, USA. Ecol. Appl. 24:1037–1056.
    • 19. Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M et al (2010). A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259:660–684 https://doi.org/10.1016/j.foreco.2009.09.001
    • 20. Barbeito I, Dawes MA, Rixen C, Senn J, Bebi P (2012). Factors driving mortality and growth at treeline: A 30-year experiment of 92 000 conifers. Ecology, 93(2):389–401 pmid:22624320
    • 21. Das AJ, Stephenson NL, Davis KP (2016) Why do trees die? Characterizing the drivers of background tree mortality. Ecology 97:2616–2627 pmid:27859135
    • 22. Jactel H, Bauhus J, Boberg J, Bonal D, Castagneyrol B, Gardiner B et al (2017) Tree diversity drives forest stand resistance to natural disturbances. Current Forestry Reports 3:223–243. https://doi.org/10.1007/s4072 5-017-0064-1
    • 23. Reilly MJ, Spies TA (2016) Disturbance, tree mortality, and implications for contemporary regional forest change in the Pacific Northwest Forest Ecology and Management 374:102–110
    • 24. FOREST EUROPE (2020) State of Europe’s Forests 2020. https://foresteurope.org/state-of-europes-forests/
    • 25. Suvanto S, Lehtonen A, Nevalainen S, Lehtonen I, Viiri H, Sandström M et al. (2021) Mapping the probability of forest snow disturbances in Finland. PLOS ONE 16(7): e0254876. pmid:34324530
    • 26. Caron MN, Kneeshaw DD, De Grandpré L, Kauhanen H, Kuuluvainen T (2009) Canopy gap characteristics and disturbance dynamics in oldgrowth Picea abies stands in northern Fennoscandia: is the forest in quasi-equilibrium? Annales Botanici Fennici. Vol. 46(4):251–262
    • 27. Fraver S, Jonsson BG, Jonsson M, Esseen P (2008) Demographics and disturbance history of a boreal old-growth Picea abies forest. Journal of Vegetation Science 19:789–798.
    • 28. Buchman RG, Pederson SP, Walters NR (1983) A tree survival model with application to species of the Great Lakes region. Can J. For. Res. 13:601–608.
    • 29. Lugo AE, Scatena FN (1996) Background and catastrophic tree mortality in tropical moist, wet, and rain forests. Biotropica 28:585–599
    • 30. Csilléry K, Seignobosc M, Lafond V, Kunstler G, Courbaud B (2013) Estimating long-term tree mortality rate time series by combining data from periodic inventories and harvest reports in a Bayesian state-space model. Forest Ecology and Management 292:64–74
    • 31. Whitney GG (1986) Relation of Michigan’s presettlement pine forests to substrate and disturbance history. Ecology 67:1548–1559
    • 32. Pitkänen A, Huttunen P, Tolonen K, Jugner H (2003) Long-term fire frequency in the spruce dominated forests of the Ulvinsalo strict nature reserve, Finland. Forest Ecology and Management 176: 305–319
    • 33. Senici D, Chen HYH, Bergeron Y, Cyr D (2010) Spatiotemporal variations of fire frequency in central boreal forest. Ecosystems 13:1227–1238
    • 34. Wallenius TH, Pitkänen A, Kuuluvainen T, Pennanen J, Karttunen H (2005) Fire history and forest age distribution of an unmanaged Picea abies—Contrasting patterns of tree mortality in late-successional Picea abies–dominated landscape. Canadian Journal of Forest Research 35:1540–1552
    • 35. Mast JN, Veblen TT (1994) A dendrochronological method of studying tree mortality patterns. Physical Geography 15:529–542
    • 36. Annila E, Petäistö R-L (1978) Insect attack on windthrown trees after the December 1975 storm in western Finland. Communicationes Instituti Forestalis Fenniae 94. 24 p.
    • 37. Zhang Q, Pregitzer KS, Reed DD (1999) Catastrophic disturbance in the presettlement forests of the Upper Peninsula of Michigan. Canadian Journal of Forest Research 29:106–114
    • 38. Seischab FK, Orwig D (1991) Catastrophic disturbances in the presettlement forests of western New York. Bulletin of the Torrey Botanical Club 118:117–122
    • 39. Worrall JD, Lee TD, Harrington TC (2005) Forest dynamics and agents that initiate and expand canopy gaps in Picea-Abies forests of Crawford Notch, New Hampshire, USA. Journal of Ecology 93:178–190
    • 40. Hofgaard A (1993) 50 years of change in a Swedish boreal old-growth Picea abies forest. Journal of Vegetation Science 4: 773–782
    • 41. Syrjänen K, Kalliola R, Puolasmaa A, Mattson J (1994) Landscape structure and forest dynamics in subcontinental Russian European taiga. Annales Zoologi Fennici 31:19–34
    • 42. Palik BJ, Pederson N (1996) Overstory mortality and canopy disturbances in longleaf pine ecosystems. Canadian Journal of Forest Research 26:2035–2047
    • 43. Lilja S, Wallenius T, Kuuluvainen T (2006). Structure and development of old Picea abies forests in northern boreal Fennoscandia. Ecoscience 13:181–192
    • 44. Battles JJ, Fahey TJ (2000) Gap dynamics following forest decline: a case study of red spruce forests. Ecological Applications 10:760–774.
    • 45. Kuuluvainen T (1994) Gap disturbance, ground microtopography, and the regeneration dynamics of boreal coniferous forests in Finland: a review. Annales Zoologici Fennici 31:35–51.
    • 46. Seidl R, Vigl F, Rössler G, Neumann M, Rammer W (2017) Assessing the resilience of Norway spruce forests through a model-based reanalysis of thinning trials Forest Ecology and Management 388:3–12 pmid:28860674
    • 47. Avila OB, Burkhart HE (1992) Modeling survival of loblolly pine trees in thinned and unthinned plantations. Can. J. For. Res. 22:1878–1882.
    • 48. Harcombe PA (1987) Tree life tables. Bioscience 37: 557–568
    • 49. Peet RK, Christensen NL (1987) Competition and tree death. BioScience 37: 586–595.
    • 50. Zhang S, Amateis RL, Burkhart HE (1997) Constraining individual tree diameter increment and survival models for loblolly pine plantations. For. Sci. 43(3):414–423
    • 51. Nothdurft A (2013) Spatio-temporal prediction of tree mortality based on long-term sample plots, climate change scenarios and parametric frailty modeling. Forest Ecology and Management 291:43–54
    • 52. Ruiz-Benito P, Lines ER, Gómez-Aparicio L, Zavala MA, Coomes DA (2013) Patterns and drivers of tree mortality in Iberian forests: Climatic effects are modified by competition. PLoS One 8 e56843. pmid:23451096
    • 53. Wyckoff PH, Clark JS (2000) Predicting tree mortality from diameter growth: a comparison of maximum likelihood and Bayesian approaches. Canadian Journal of Forest Research 30:156–167
    • 54. Bravo-Oviedo A, Sterba H, Del Río M, Bravo F (2006) Competition-induced mortality for Mediterranean Pinus pinaster Ait. and P. sylvestris L. Forest Ecology and Management 222:88–98 https://doi.org/10.1016/j.foreco.2005.10.016
    • 55. Lännenpää A, Aakala T, Kauhanen H, Kuuluvainen T (2008) Tree mortality agents in pristine Norway spruce forests in northern Fennoscandia. Silva Fennica 42:151–163
    • 56. Luo Y, Chen HYH (2011) Competition, species interaction and ageing control tree mortality in boreal forests. Journal of Ecology 99:1470–1480 https://doi.org/10.1111/j.1365-2745.2011.01882.x
    • 57. Stephenson NL, van Mantgem PJ, Bunn AG, Bruner H, Harmon ME, O’Connell KB et al (2011) Causes and implications of the correlation between forest productivity and tree mortality rates. Ecological Monographs 81:527–555 https://doi.org/10.1890/10-1077.1
    • 58. Guan BT, Gertner GZ (1991a) Using a parallel distributed processing system to model individual tree mortality. For. Sci. 37(3):871–885.
    • 59. Guan BT, Gertner GZ (1991b) Modeling red pine tree survival with an artificial neural network. For. Sci. 37(5):1429–1440.
    • 60. Hülsmann L, Bugmann H, Brang P (2017) How to predict tree death from inventory data—lessons from a systematic assessment of European tree mortality models. Canadian Journal of Forest Research 900:890–900 https://doi.org/10.1139/cjfr-2016-0224
    • 61. Dursky J (1997) Modellierung der Absterbeprozesse in Rein- und Mischbestanden aus Fichte und Buche. Allg. Jagd- und Forstzeitung 168:131–134.
    • 62. Dietze MC, Moorcroft PR (2011) Tree mortality in the eastern and central United States: Patterns and drivers. Global Change Biology 17:3312–3326 https://doi.org/10.1111/j.1365-2486.2011.02477.x
    • 63. Condés S, Del Río M (2015) Climate modifies tree interactions in terms of basal area growth and mortality in monospecific and mixed Fagus sylvatica and Pinus sylvestris forests. European Journal of Forest Research 134:1095–1108 https://doi.org/10.1007/s1034 2-015-0912-0
    • 64. Archambeau J, Ruiz-Benito P, Ratcliffe S, Fréjaville T, Changenet A, Muñoz Castañeda JM et al (2020). Similar patterns of background mortality across Europe are mostly driven by drought in European beech and a combination of drought and competition in Scots pine. Agricultural and Forest Meteorology, 280, 107772. https://doi.org/10.1016/j.agrformet.2019.107772
    • 65. Chen C, Weiskittel A, Bataineh M, MacLean DA (2017) Evaluating the influence of varying levels of spruce budworm defoliation on annualized individual tree growth and mortality in Maine, USA and New Brunswick, Canada. Forest Ecology and Management 396:184–194
    • 66. Acker SA, Boetsch JR, Bivin M, Whiteaker L, Cole C, Philippi T (2015) Recent tree mortality and recruitment in mature and old-growth forests in western Washington. Forest Ecology and Management 336:109–118
    • 67. Aakala T, Kuuluvainen T, De Grandpre L, Gauthier S (2007) Trees dying standing in the northeastern boreal old-growth forests of Quebec: spatial patterns, rates, and temporal variation. Canadian Journal of Forest Research 37:50–61
    • 68. Sirén G (1955) On the development of spruce forests on raw humus sites in northern Finland and its ecology. Acta Forestalia Fennica 62:1–408
    • 69. Ozolincius R, Miksys V, Stakenas V (2005) Growth-independent mortality of Lithuanian forest tree species. Scand. J. Forest Res. 20:153–160
    • 70. Greenwood DL, Weisberg PJ (2008) Density-dependent tree mortality in pinyonjuniper woodlands. Forest Ecol. Manag. 255:2129–2137
    • 71. Juknys R, Vencloviene J, Jurkonis N, Bartkevicius E, Sepetiene J (2006) Relation between individual tree mortality and tree characteristics in a polluted and non-polluted environment. Environ. Monitor. Assess. 121:519–542
    • 72. Dobbertin M (2002). Influence of stand structure and site factors on wind damage comparing the storms Vivian and Lothar. Forest Snow and Landscape Research, 77(1/2), 187–205.
    • 73. Bouchard M., Pothier D., and Ruel J.-C. (2009). Stand-replacing windthrow in the boreal forests of eastern Quebec. Canadian Journal of Forest Research. 39(2): 481–487. https://doi.org/10.1139/X08-174
    • 74. Valinger E Fridman J (2011). Factors affecting the probability of windthrow at stand level as a result of Gudrun winter storm in southern Sweden. Forest Ecology and Management 26(3): 398–403, ISSN 0378-1127, https://doi.org/10.1016/j.foreco.2011.04.004.
    • 75. Luo Y, Chen HYH (2013) Observations from old forests underestimate climate change effects on tree mortality. Nature Communications 4:1655–1656 pmid:23552070
    • 76. van Mantgem PJ, Stephenson NL, Byrne JC, Daniels LD, Franklin JF, Fule PZet al (2009) Widespread increase of tree mortality rates in the western United States. Science 323(5913):521–524 pmid:19164752
    • 77. Neumann M, Mues V, Moreno A, Hasenauer H, Seidl R (2017) Climate variability drives recent tree mortality in Europe. Global Change Biology 23:4788–4797. https://doi.org/10.1111/gcb.13724
    • 78. Loehle C, LeBlanc D (1996) Climate change effects on forests: A critical review. United States: N. p., 1996. Web.
    • 79. Young DJN, Stevens JT, Earles JM, Moore J, Ellis A, Jirka AL et al (2017) Long-term climate and competition explain forest mortality patterns under extreme drought. Ecology Letters 20:78–86 pmid:28000432
    • 80. Garzón B, González Muñoz M, Wigneron N, Moisy C, Fernández-Manjarrés J, Delzon S (2018). The legacy of water deficit on populations having experienced negative hydraulic safety margin. Global Ecology and Biogeography 27:346–356 https://doi.org/10.1111/geb.12701
    • 81. Jump AS, Ruiz-Benito P, Greenwood S, Allen CD, Kitzberger T, Fensham R et al (2017). Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Global Change Biology 23:3742–3757 pmid:28135022
    • 82. Wood JD, Knapp BO, Muzika RM, Stambaugh MC, Gu L (2018) The importance of drought–pathogen interactions in driving oak mortality events in the Ozark Border Region. Environmental Research Letters 13 015004 https://doi.org/10.1088/1748-9326/aa94fa
    • 83. Anderegg WRL, Hicke JA, Fisher RA, Allen CD, Aukema J, Bentz B et al (2015) Tree mortality from drought, insects, and their interactions in a changing climate. New Phytologist, 208:674–683 pmid:26058406
    • 84. Güneralp B, Gertner G (2007) Feedback loop dominance analysis of two tree mortality models: relationship between structure and behavior. Tree Physiology 27:269–80 pmid:17241969
    • 85. Allen CD, Breshears DD, McDowell NG (2015). On underestimation of global vulnerability to tre mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6:1–55
    • 86. Knoke T, Paul C, Gosling E, Jarisch I, Mohr J, Seidl R (2022) Assessing the Economic Resilience of Different Management Systems to Severe Forest Disturbance. Environ Resource Econ pmid:36712582
    • 87. Andersson E, Keskitalo E C H (2018). Adaptation to climate change? Why business-as-usual remains the logical choice in Swedish forestry. Global Environ. Change 48:76–85.
    • 88. Espmark K (2017). Debatten om hyggesfritt skogsbruk i Sverige: En analys av begrepp och argument i svenskt pressmaterial 1994–2013. Future Forests Rapportserie 2017:2. www.slu.se/futureforests
    • 89. Ekholm A, Axelsson P, Hjältén J, Lundmark T, Sjögren J. (1922) Short-term effects of continuous cover forestry on forest biomass production and biodiversity: Applying single-tree selection in forests dominated by Picea abies. Ambio 51:2478–2495. https://doi.org/10.1007/s13280-022-01749-5
    • 90. Hertog I M, Brogaard S, Krause T (2022) Barriers to expanding continuous cover forestry in Sweden for delivering multiple ecosystem services, Ecosystem Services 53(101392):2212–0416. https://doi.org/10.1016/j.ecoser.2021.101392.
    • 91. Leslie A (1966) A review of the concept of the normal forest. Australian Forestry 30(2):139–147
    • 92. Kärenlampi PP (2022a) Two Sets of Initial Conditions on Boreal Forest Carbon Storage Economics. PLOS Clim 1(2): e0000008. https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000008
    • 93. Kärenlampi PP (2022b) Two Manifestations of Market Premium in the Capitalization of Carbon Forest Estates. Energies 2022(15):3212 https://doi.org/10.3390/en15093212
    • 94. Continuous-cover silviculture benefits neither forest ecosystems nor climate 25.10.2018 / Article https://forest.fi/article/continuous-cover-silviculture-benefits-neither-forest-ecosystems-nor-climate/#acf755bc Accessed Jan. 21, 2024.
      • 95. Nevalainen S (2017). Comparison of damage risks in even- and uneven-aged forestry in Finland. Silva Fennica 51(3): 1741. https://doi.org/10.14214/sf.1741
      • 96. Lundmark T, Bergh J, Nordin A, Fahlvik N, Poudel BC. (2016) Comparison of carbon balances between continuous-cover and clear-cut forestry in Sweden. Ambio 45: 203–213. pmid:26744054
      • 97. Hynynen J, Eerikäinen K, Mäkinen H, Valkonen S (2019) Growth response to cuttings in Norway spruce stands under even-aged and uneven-aged management. Forest Ecology and Management 437:314–323
      • 98. Bianchi S, Huuskonen S, Siipilehto J, Hynynen J (2020) Differences in tree growth of Norway spruce under rotation forestry and continuous cover forestry. Forest Ecology and Management 458, 117689. 7 p.