Quantifying natural disturbances using a large-scaledendrochronological reconstruction to guide forest management

VOJT�ECH �CADA ,1,12 VOLODYMYRTROTSIUK ,1,2 PAVEL JANDA,1 MARTIN MIKOL�A�S,1,3 RADEK BA�CE,1

THOMAS A. NAGEL,1,4 ROBERT C. MORRISSEY,1 ALAN J. TEPLEY ,5 OND�REJ VOSTAREK ,1 KRE�SIMIR BEGOVI�C,1

OLEH CHASKOVSKYY,6 MARTIN DU�S�ATKO,1 ONDREJ KAMENIAR,1 DANIEL KOZ�AK,1 JANA L�ABUSOV�A,1 JAKUB M�ALEK,1

PETER MEYER,7 JOSEPH L. PETTIT,1 JONATHAN S. SCHURMAN,1 KRIST�YNA SVOBODOV�A,1,8 MICHAL SYNEK,1

MARIUS TEODOSIU,9,10 KAROLUJH�AZY,11 AND MIROSLAV SVOBODA1

1Department of Forest Ecology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kam�yck�a 129,Praha 6 – Suchdol, Prague 165 00 Czech Republic

2Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Z€urcherstrasse 111, Birmensdorf CH-8903 Switzerland3PRALES, Odtrnovie 563, Rosina SK-01322 Slovakia

4Department of Forestry and Renewable Forest Resources, University of Ljubljana, Ve�cna pot 83, Ljubljana 1000 Slovenia5Division of Biological Sciences, W.A. Franke College of Forestry & Conservation, University of Montana, Missoula, Montana 59812

USA6Faculty of Forestry, Ukrainian National Forestry University, Gen. Chuprynka 103, Lviv 790 57 Ukraine

7North West German Forest Research Institute, Gr€atzelstrasse 2, G€ottingen D-37079 Germany8Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kam�yck�a 129, Praha 6 – Suchdol, Prague 165 00 Czech

Republic9“Marin Dr�acea” National Research-Development Institute in Forestry, Station Campulung Moldovenesc, Calea Bucovinei 73b,

Campulung Moldovenesc, Suceava 725100 Romania10Ștefan cel Mare University of Suceava, Universit�at�ii 13, Suceava 720229 Romania

11Technical University in Zvolen, T.G. Masaryka 24, Zvolen 96053 Slovakia

Citation: �Cada, V., V. Trotsiuk, P. Janda, M. Mikol�a�s, R. Ba�ce, T. A. Nagel, R. C. Morrissey, A. J. Tepley,O. Vostarek, K. Begovi�c, O. Chaskovskyy, M. Du�s�atko, O. Kameniar, D. Koz�ak, J. L�abusov�a, J. M�alek, P.Meyer, J. L. Pettit, J. S. Schurman, K. Svobodov�a, M. Synek, M. Teodosiu, K. Ujh�azy, and M. Svoboda.2020. Quantifying natural disturbances using a large-scale dendrochronological reconstruction to guideforest management. Ecological Applications 30(8):e02189. 10.1002/eap.2189

Abstract. Estimates of historical disturbance patterns are essential to guide forest manage-ment aimed at ensuring the sustainability of ecosystem functions and biodiversity. However,quantitative estimates of various disturbance characteristics required in management applica-tions are rare in longer-term historical studies. Thus, our objectives were to (1) quantify pastdisturbance severity, patch size, and stand proportion disturbed and (2) test for temporal andsubregional differences in these characteristics. We developed a comprehensive dendrochrono-logical method to evaluate an approximately two-century-long disturbance record in theremaining Central and Eastern European primary mountain spruce forests, where wind andbark beetles are the predominant disturbance agents. We used an unprecedented large-scalenested design data set of 541 plots located within 44 stands and 6 subregions. To quantify indi-vidual disturbance events, we used tree-ring proxies, which were aggregated at plot and standlevels by smoothing and detecting peaks in their distributions. The spatial aggregation of dis-turbance events was used to estimate patch sizes. Data exhibited continuous gradients fromlow- to high-severity and small- to large-size disturbance events. In addition to the importanceof small disturbance events, moderate-scale (25–75% of the stand disturbed, >10 ha patch size)and moderate-severity (25–75% of canopy disturbed) events were also common. Moderate dis-turbances represented more than 50% of the total disturbed area and their rotation periodsranged from one to several hundred years, which is within the lifespan of local tree species. Dis-turbance severities differed among subregions, whereas the stand proportion disturbed variedsignificantly over time. This indicates partially independent variations among disturbancecharacteristics. Our quantitative estimates of disturbance severity, patch size, stand proportiondisturbed, and associated rotation periods provide rigorous baseline data for future ecologicalresearch, decisions within biodiversity conservation, and silviculture intended to maintainnative biodiversity and ecosystem functions. These results highlight a need for sufficiently largeand adequately connected networks of strict reserves, more complex silvicultural treatments

Manuscript received 18 November 2019; revised 8 February2020; accepted 17 March 2020. Corresponding Editor: CarolynH. Sieg.

12 E-mail: cada@fld.czu.cz

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that emulate the natural disturbance spectrum in harvest rotation times, sizes, and intensities,and higher levels of tree and structural legacy retention.

Key words: disturbance-based forestry; ecosystem management; intermediate disturbances; Ips typogra-phus; mixed-severity disturbance regime; mortality; nonequilibrium forest dynamics; old-growth forests;Picea abies; primary forests; retention forestry; windthrows.

INTRODUCTION

Inappropriate forest management that alters foreststructures, compositions, or functions as a result of thereplacement or alteration of historical disturbance pat-terns cause serious ecological harm, such as the loss ofbiodiversity or soil degradation (Achat et al. 2015,Mikol�a�s et al. 2017b). As characterized in the concept ofthe “historical range of variability” (Keane et al. 2009),specific variations of past disturbances were the primarydrivers of forest conditions that native forest species hadadapted to over long periods of time, and they are likelyto benefit from similar conditions in the future (Drapeauet al. 2016, Mikol�a�s et al. 2017a, Betts et al. 2019). Usingthe historical range of variability (e.g., by emulating nat-ural disturbances) to guide forest management will helpsustain forest ecosystem structures, composition, andfunctions (Seymour et al. 2002, Long 2009, Kulakowskiet al. 2017). Natural disturbance characteristics can pro-vide a guide to key management decisions, such as har-vest rotation times, size, and intensity, that can be linkedto natural disturbance frequency, patch size, and sever-ity, respectively (Seymour et al. 2002, Keane et al. 2009).Natural disturbance patterns can also provide answersas to what, how much, and where forest structuresshould be left as a part of retention forestry approaches(Fedrowitz et al. 2014, Mori and Kitagawa 2014).Dendrochronological reconstructions are among the

most useful methods to provide long, spatially explicitrecords of disturbance characteristics (Lorimer and Fre-lich 1989, Svoboda et al. 2014, Schurman et al. 2018).Estimates of disturbance frequency are commonlyemphasized in dendrochronological approaches. How-ever, to the detriment of management applicability ofdendrochronological results, quantitative estimates ofmultiple disturbance characteristics have rarely beendeveloped (Keane et al. 2009), likely due to the temporalimprecision (e.g., delayed response of tree growth to dis-turbance or false positive reactions), complexity, orinsufficient spatial coverage of the resulting records. Asan example, long-term records of disturbance patch sizesare unavailable, despite being of extreme interest as atemplate for harvest size (a key attribute of forest man-agement policies) or as a feature influencing landscapefluctuations and beta diversity (Lehnert et al. 2013, Dra-peau et al. 2016). Here, we attempt to develop and applya comprehensive quantitative method to estimate pastdisturbance characteristics (disturbance severity, patchsize, and stand proportion disturbed) that can be directlyused to develop sustainable forest management planningand policy.

Studies analyzing disturbance characteristics havemostly focused either on small, low-severity disturbanceevents, which cause the mortality of individual or smallgroups of trees and create gap-scale openings, or large,high-severity, catastrophic events, which kill nearly allthe mature trees across large areas (Seymour et al. 2002).This could be partly because small events are frequentand readily visible in contemporary forest structures,while catastrophic events are often highlighted due totheir extensive impact. More recently, moderate-scaleand moderate-severity (also referred to as intermediateseverity) disturbances are receiving increasing attention(e.g., see Stueve et al. 2011, Nagel et al. 2017, Meigs andKeeton 2018 and their reference lists); these events causea peak in mortality over larger areas, but a substantialnumber of surviving mature trees are typically retained.Several analyses of moderate disturbance impacts havesuggested that the cumulative sizes of these events mayaffect larger areas than catastrophic events, and theytypically have a stronger influence on forest structureand composition than small, more frequent disturbances(Stueve et al. 2011, Nagel et al. 2017, Meigs and Keeton2018). These studies also propose that the rotation peri-ods of moderate disturbances are likely shorter than thelifespan of the dominant tree species. However, few stud-ies have compared the relative frequency of moderatedisturbances to small and large events in large-scale andlong-term landscape dynamics (Fraver et al. 2009, Nagelet al. 2017). We explore this comparison to determine ifmoderate disturbances were more common and moreimportant drivers of forests than is typically recognized,which is also central for forest management considera-tions.Currently, there are no management guidelines based

on natural forest disturbance dynamics for productionforests in the mountains of Central and Eastern Europe(Kraus and Krumm 2013). These forests are dominatedby Norway spruce (Picea abies), and they are managedprimarily by even-aged methods using either shelter-wood regeneration or regeneration on clearcuts (Cardel-lini et al. 2018). Harvest areas typically range between 1and 3 ha in size, and they are managed on a rotationperiod between 80 and 120 yr (average 100 yr; Cardelliniet al. 2018). Increasing recognition of the environmentaland production losses caused by even-aged methods hasled to the promotion of single- or group-selection man-agement systems because these approaches are perceivedas “closer-to-nature” under the philosophy of continuouscover management (Kraus and Krumm 2013, Branget al. 2014). Although these approaches seek to produceconditions similar to natural forests, no studies have

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compared the sizes, frequencies, and intensities ofmanagement treatments vs. the characteristics of naturaldisturbances. We believe that quantitative recommenda-tions are best suited for direct implementation into man-agement and policy. In particular, values of disturbancecharacteristics around the upper end of the size-severitycontinuum are helpful to set limits (e.g., maximum sizesor severities) for management treatment regulations.Recent natural disturbances in Central and Eastern

European mountain spruce forests have caused extensivemortality across large areas in a few landscapes, whileother regions have been subject to more localized small-or moderate-scale events (Senf and Seidl 2018). Like-wise, plot- and stand-level dendrochronological recon-structions suggest that mixed-severity disturbanceregimes with wide variation of low to high disturbanceseverities historically operated in mountain spruce for-ests (Svoboda et al. 2014, Trotsiuk et al. 2014, �Cadaet al. 2016). The prevailing disturbance agents are wind-storms and bark beetle (Ips typhographus) outbreaks(�Cada et al. 2016, Senf and Seidl 2018), although beetledisturbances are often triggered by windthrow eventsand local weather conditions, particularly droughts. Atlow population densities, beetles breed in fresh wind-thrown, weakened, or injured spruce trees that can causebeetle population size increases as this material accumu-lates (Wermelinger 2004, Marini et al. 2017). As the pop-ulation size approaches the outbreak phase, the beetleswill colonize and kill healthy, mature trees. These rela-tionships enable high wind speeds and droughts to syn-chronize natural disturbances in mountain spruceforests, but these events are still spatially diverse and pat-chy, likely because mortality is also influenced by treeand stand sensitivity (predominantly increasing with treesize and age; Senf and Seidl 2018). Such a complex com-bination of disturbance sizes and severities seems to be acommon characteristic of temperate forests predomi-nantly driven by wind and insect disturbances (Fraveret al. 2009, Stueve et al. 2011). Therefore, Europeanmountain spruce forests are representative of other for-est types driven by wind and insect disturbances, andthey provide a valuable test bed for quantifying variouscharacteristics of past disturbance events using themethodological tools employed in this study.Our objective was to quantify multiple characteristics

of past disturbances, including the severity, patch size,and stand proportion disturbed, across an unprecedent-edly large-scale nested design plot network in theremaining primary mountain spruce forests of Centraland Eastern Europe. Building on long-tested dendroeco-logical methods, we developed a comprehensiveapproach to quantify key disturbance characteristics. Inaddition, we tested for potential temporal and subre-gional differences in disturbance characteristics tounderstand the confidence range of our estimates. Ourresults provide valuable insight into the importance ofmoderate-scale and moderate-severity disturbanceswithin a spectrum of natural events. Finally, we discuss

how these results can be translated into managementpractices within the study region.

METHODS

Study forests

We used a plot network established in the remaining pri-mary mountain spruce forests of Central and Eastern Eur-ope (remoteforests.org 2020; Schurman et al. 2018). Datawere compiled from published regional studies across alarge geographical gradient covering the CarpathianMountains of southern and northern Romania (Svobodaet al. 2014), Ukraine (Trotsiuk et al. 2014), southern andnorthern Slovakia (Janda et al. 2017), the Bohemian Forestin the Czech Republic (�Cada et al. 2016), and the HarzMountains in Germany (Meyer et al. 2017; Fig. 1). Standswith no evidence of direct human influence, such as log-ging or livestock grazing, were selected with the help oflocal experts or primary forest inventories (Mikol�a�s et al.2019). In stands of the Bohemian Forest, the prevailinginfluence of natural disturbances was evident by the signifi-cant association of reconstructed events and windstormand bark beetle (Ips typographus) outbreak events in his-torical records (see �Cada et al. 2016 for more details aboutpossible human influence in these stands).Norway spruce accounted for 97% of total tree basal

area in our study forests with minor and regionally variablecomponents of Pinus cembra, Sorbus aucuparia, and Abiesalba, among several other less common species. Theground vegetation layers were dominated by Calamagrostisvillosa, Vaccinium myrtillus, Avenella flexuosa, Athyriumdistentifolium, and Luzula sylvatica. Tree density variedbetween 0 and 1,620 trees/ha (median: 430), and the livetree basal area ranged from 0 to 105 m2/ha (median: 46).Lying deadwood volume ranged from 1 to 478 m3/ha (me-dian: 105). Detailed distributions of selected structuralparameters are presented in Appendix S1: Fig. S1.Our study forests occupy relatively high altitudes

ranging from 1,150 to 1,700 m above sea level near thealpine zone. Mean annual temperature varies between1.5°C and 4°C, with mean growing season (May–Octo-ber) temperature ranges of 7.5–10°C, and an annual pre-cipitation of about 800–2,000 mm (data availableonline).13 Bedrock and soils are variable throughout therange, with Podzols being the predominant soil type, butwith Cambisols present at lower elevations and Lep-tosols on stony exposed sites (Valtera et al. 2013).

Data collection

We used a hierarchical sample design of 541 plotsnested within 44 stands (4–63 plots/stand; median: 11)nested within six subregions (4–13 stands/subregion;median: 6). The “West” subregion was comprised of theHarz and Bohemian Forest mountain ranges to avoid

13www.worldclim.org

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any subregion with a single stand (Fig. 1). Stand sizesranged from 8 to 154 ha (median: 50) with a mediandensity of one plot per 4.5 ha (range: one plot per 2.1–9.8 ha). Stands were defined as a compact area, withinwhich the plots were regularly distributed with similardensity between stands. To accomplish that, we subdi-vided seven stands that were fragmented in their originalstudies, and we excluded 35 plots from the original data-base to homogenize plot densities among stands. Circu-lar plots of 1,000 m2 (or 500 m2 in some densehomogeneous plots of northern Romania) were placedrandomly within a regular grid covering the stand area.Within each plot, we recorded the species and diame-

ter of all living trees >10 cm in diameter, and we mea-sured the horizontal crown area of five trees. Weextracted an increment core 1 m above the ground from25 randomly selected non-suppressed trees (i.e., a treethat is exposed to direct sunlight and not overtopped byany neighboring trees) on each plot; in some cases, suchas the dense homogeneous plots in northern Romania,only 15–20 trees were sampled. In total, about 13,500cores were processed using standard dendrochronologi-cal techniques, and ring-width series were measured andcrossdated using a LINTAB sliding table and TsapWinsoftware (RINNTECH, Heidelberg, Germany). Meanwithin-stand correlation between the individual rawring-width series was 0.22 (range 0.09–0.42). Currentcrown areas of the cored trees were modelled using therelationships between crown area and diameter sepa-rately for each combination of species and subregion(Appendix S1: Table S1).

Disturbance proxy evidence in tree-growth series

Dendrochronological methods for evaluating pastdisturbance events rely on proxy evidence of distur-bances recorded in the tree-ring series of trees thatsurvived or were newly recruited after the disturbanceevents. We build on the method originally publishedby Lorimer and Frelich (1989), which relies on indi-rect proxy evidence (i.e., synchronous rapid earlygrowth rates and growth releases) that is particularlyuseful in systems driven, for example, by wind, bark-beetle, or high-severity disturbances generally, whereno clear direct evidence is available. However, webelieve this approach is useful across a range of for-ests because in addition to the detection of distur-bance events it also allows for the estimation ofdisturbance severity (see below).Two types of radial growth patterns were assumed to

indicate past disturbance: (1) release from suppression,characterized by an abrupt, large, and sustained growthincrease, suggests a response to the mortality of sur-rounding trees, and (2) gap origin, characterized byrapid early growth rates, suggests the recruitment of atree in open conditions after disturbance (Lorimer andFrelich 1989). A tree was determined to be a gap origintree if the mean ring width of the fifth to fifteenth ringfrom the pith exceeded the early growth rate threshold,as previously defined by comparing early growth ratesin young trees sampled in gaps vs. those growing undera forest canopy (Appendix S1: Table S2; Svoboda et al.2014, Trotsiuk et al. 2014, Janda et al. 2017). The year

FIG. 1. Location of the study area of the remaining primary mountain Norway spruce forests spanning a large geographicalgradient in Central and Eastern Europe. We distinguished six subregions within the study area containing 44 stands in total. Base-map service layers from ESRI, NOAA, and USGS are also displayed.

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when the gap origin tree was recruited to coring heightwas used as proxy evidence of a disturbance event. Theyear of a release from suppression was determined tobe proxy evidence of a disturbance using the absoluteincrease method (Fraver and White 2005). The releasewas a year in the growth-series when the differencebetween the following 10-yr mean and the preceding10-yr mean peaked and exceeded a threshold value thatwas defined as 1.25 times the standard deviation usingthe growth differences (absolute increase values) in thewhole data set. We excluded growth increases thatlasted less than seven years by comparison of laggedand leaded 10-yr means with the original precedingand following 10-yr means. We considered only releasesthat happened more than 20 yr after potential rapidearly growth rate, and before the tree reached thethreshold diameter that we assumed indicated it was areleased tree of the canopy. The threshold diameterwas based on comparisons of tree diameters of cur-rently suppressed vs. released trees. We allowed formultiple proxy evidences of disturbances within indi-vidual trees. All the thresholds used were species- andsubregion specific (Appendix S1: Table S1). Of all thedisturbance proxy evidence recorded, 44% were rapidearly growth rates and 41% were releases from suppres-sion; the remaining 15% were represented by trees thatdid not fulfil any of our criteria, and those trees weretreated as gap origin trees because sampling includedonly trees already recruited to the canopy (Lorimerand Frelich 1989).

Disturbance events and disturbance characteristics

Disturbance signal based on indirect proxy evidencein tree-ring series can be obscured by temporal lags intree growth responses to disturbance events, and addi-tional variability in tree-ring series unrelated to distur-bances that result in both false positive and falsenegative detections of disturbances (Trotsiuk et al.2018). To contend with the temporal imprecision, tradi-tional methods typically rely on decadal binning of thedata and the construction of a plot-level disturbancechronology, thus representing a composite of all distur-bance proxy evidence throughout the plot history (Lori-mer and Frelich 1989). Decadal binning may falselydetect multiple disturbance events (instead of a singlereal event) if the proxy evidence is dispersed over two ormore consecutive decades, which would also result in anunderestimation of disturbance severity. We improvedupon the traditional technique by smoothing the datacontinuously rather than binning, which allowed us todistinguish individual disturbance events and addresssignal obscurity and false detections (described in detailbelow). In general, the compilation of disturbance proxyevidence identified for each tree produced a plot-levelrecord of disturbance events, which was used to interpretdisturbance severities. The compilation of plot-level dis-turbance events provided a stand-level record of

disturbance events to interpret patch sizes and standproportions disturbed.At each plot, we smoothed the years of all the distur-

bance proxy evidence, which were weighted using thetrees’ relative current crown areas (i.e., current crownarea of a tree divided by the sum of current crown areasof all trees cored on the plot), using a running kerneldensity function (Appendix S1: Fig. S2, Data S1; Trot-siuk et al. 2018). Kernel density is a nonparametricmethod to estimate the probability density function ofrandom variables (Duong 2007). For the moving 30-yrwindow, we calculated the density with a smoothingbandwidth equal to 5. We used the R software for theseanalyses (R Core Team 2018). From the resulting curve,we estimated the years of individual disturbance eventsby extracting peak years before which the curve wasincreasing for at least 5 yr. We also required that the dis-tance between two peaks was more than 10 yr. Theseverity of an individual disturbance event was estimatedas the relative canopy area disturbed on the plot, asdetermined by summing the relative current crown areasof trees whose disturbance proxy evidence occurredwithin the 11-yr window around the peak. This calcula-tion relies on the conventional assumption that mosttrees respond to disturbances within a decade of theevent and that the sum of relative current crown areas oftrees that indicate past disturbance is representative ofthe proportion of the plot previously disturbed (Lorimerand Frelich 1989). To avoid false positives that mightreflect variation in tree growth that is unrelated to dis-turbances, we removed peaks with a disturbance severitybelow 10%. The results should be interpreted carefullybecause the disturbance severity can vary by more than10% due to methodological decisions in the den-drochronological reconstructions (�Samonil et al. 2015,�Cada et al. 2016, Trotsiuk et al. 2018). We believe thatour estimates are rather conservative in an attempt toavoid false positives in the data; thus, we suggest that themethod is more sensitive to underestimation rather thanoverestimation of disturbance severities.To determine the proportion of stands affected by each

disturbance, we first grouped plot-level disturbanceevents at the stand level using a similar approach to theplot-level summaries (Appendix S1: Fig. S3, Data S1).All the disturbance years from the plot level weresmoothed using a running kernel density with the samecoefficients, and peaks were again extracted by the samemethod as applied at the plot level. The analysis was lim-ited to the period between 1810 and 1990; prior to 1810,the sample size was generally low, and tree-ring databeyond 1990 are not representative because the minimumtree size in our data excludes more recent tree recruit-ment. At the beginning of the period, our sample sizecontained 2,452 trees (18%), 328 plots (61%) had treesrepresenting at least 10% of the canopy, and 42 stands(95%) had at least one available plot (see Appendix S1:Fig. S4 for trends in sample size). To account for the vari-able numbers of plots per stand, to avoid exclusion of

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rare events, and to increase the robustness of the stand-level disturbance event estimations, we conducted a boot-strapped analysis on random subsets of the plots withineach stand. We randomly selected 10 plots and calculatedkernel density and extracted peaks, and we repeated thisprocedure 1,000 times. We smoothed the resulting distri-butions of bootstrapped peaks by a running kernel den-sity using an 11-yr window and bandwidth equal to 1,and we then extracted all stand-level peaks (i.e., stand-level years of disturbance events) separated by at least10 yr. Each plot-level event was associated with the near-est stand-level event because we assumed there were noremaining false positive detections of disturbance eventsin the plot-level data. The median difference betweenplot-level disturbance events and the stand-level peakswas two years, and 95% of the events were no more than8 yr apart. This analysis cannot distinguish a single dis-turbance event from two consecutive events happeningwithin one or two decades, so we consider all such casesas a single event. The delayed tree reactions to the distur-bance event by more than a decade could potentiallycause a single disturbance event to be classified as twoconsecutive events.We estimated the patch size of each stand-level distur-

bance event based on the spatial distribution of plotsshowing evidence of the event (Appendix S1: Fig. S5,Data S1). Stand polygons were divided into a set ofsmaller polygons with one polygon for each plot usingThiessen polygons in ArcGIS 10.5 (ESRI, Redlands,California, USA). Plot-level polygons were labelled bythe stand-level disturbance year evidenced on the match-ing plot, neighboring polygons that shared the same dis-turbance year were merged into a single patch, and thetotal patch area was calculated. We limited the patch sizerepresented by a single plot to a maximum of 4 ha,which is just below the minimal limit that we could dis-tinguish based on our plot density (see above). In thepatch size analysis, we assumed that the unsampled areabetween two neighboring plots with evidence of the samedisturbance event was similarly affected by this event.Although this assumption is likely not valid in all cases,we believe the unbiased randomized plot distributionensures that the positive and negative errors in patch sizeestimations tended to balance out. We expect that manydisturbance patches had irregular shapes and spatiallyvariable severity across affected areas; however, we wereable to analyze only sizes of the areas disturbed withmore than 10% severity with the available data. Usingthis approach, we estimated years of disturbances andtheir associated patch sizes for each stand. We analysedpatch sizes only in stands larger than 20 ha, which lim-ited the analysis to a total of 40 stands.Variation in plot-level disturbance severities, stand-

level proportion of plots disturbed, and patch sizes wereexpressed in rotation periods based on the number ofevents per length of the chronology. The beginning ofeach chronology was associated with the year of the firstdisturbance event and the end year of each chronology

was 1990. Rotation period was defined as the inverse ofthe disturbance frequency. The estimate of rotation peri-ods is useful for forest management (Keane et al. 2009)because it provides the average time interval betweensuccessive disturbance events and the average proportionof the landscape affected within a given time period(Fraver et al. 2009). For example, we found 1,368 plot-level disturbance events in total, and the total length ofall plot-level chronologies was 84,447 yr, thus the aver-age rotation period for disturbances that remove morethan 10% of the forest canopy (severity) was estimatedto be 62 yr (84,447 yr/1,368 events). We calculated 95%confidence intervals of the rotation periods based onbootstrapping techniques using 1,000 random subsets of50 plots or 20 stands, as appropriate.

Statistical analyses

Because the patch size estimation was based on severalassumptions, as discussed above, we complemented thisanalysis with a statistically unbiased estimation of spa-tial autocorrelation of disturbance events using Moran’sI in the ncf package (Bjørnstad 2018) of the R software(R Core Team 2018). We used Moran’s I to test for boththe degree of spatial autocorrelation in disturbanceevents and how the scale of spatial autocorrelation var-ied over time. We tested for autocorrelation among yearsof disturbance events after excluding extreme values ofdisturbance years before 1740 and after 1990. Then wecalculated Moran’s I in the 30-yr running time windowto obtain a temporal trend of spatial autocorrelation.The disturbance severities within the 30-yr window (0was used in cases with no detected disturbance eventwithin the window) were used as input data in this analy-sis. We used 50-m distance classes and 1,000 MonteCarlo permutations to test the significance of spatialassociations. The intercept of the correlogram modelwas used as a measure of the distance threshold belowwhich the disturbance events are spatially autocorre-lated, and it was also used as a proxy for patch sizeradius.We tested the effect of subregion and time on the dis-

turbance characteristics using multivariate analysis ofvariance (MANOVA) in the R software (R Core Team2018). The disturbance data were initially averaged foreach combination of stand and disturbance year, andthe time interval (1810–1990) was divided into six peri-ods to be used as a factor variable in the models toaccount for potentially nonlinear temporal effects. Themost parsimonious model was selected by comparingcomplex and less complex models using ANOVA. Multi-ple post-hoc comparisons between subregions and peri-ods were performed using Tukey’s HSD test.

RESULTS

In 541 permanent plots established across Europeanprimary mountain spruce forests, we identified 1,368

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disturbance events that ranged in severity from 10% to100%. Across the 40 stands larger than 20 ha, we identi-fied 287 stand-level disturbance events (303 across all 44stands), represented by 573 discrete patches (some of theevents included more than one non-contiguous patchwithin the same stand). Stand-level events suggested amaximum patch size of more than 93 ha with as muchas 100% of the stand disturbed. Median values (and 95%ranges) for the disturbance severities were 18.7% (10.4–65.6), for the patch sizes 4 ha (1.8–28.1), and for thestand proportions disturbed 25.0% (6.1–77.3). Thereconstructed disturbance events generally aligned withdocumented historic windstorms and bark-beetle out-breaks (see the regional studies for more details; Svo-boda et al. 2014, �Cada et al. 2016, Janda et al. 2017,Meyer et al. 2017).The data revealed a continuous gradient from low-

severity, small-scale disturbance events to higher-sever-ity, larger-scale events, and this gradient is intimatelyrelated to the rotation period (frequency; Fig. 2), whichshows that lower-severity and smaller-scale events areincreasingly more frequent. Disturbances that removedtens of percent of canopy over areas up to tens of hec-tares occurred within a period comparable to sprucelifespan, which ranges from 150 to 450 yr (Appendix S1:Fig. S1). These events remove more than a single orsmall group of trees, but they also leave a significant por-tion of surviving trees. High-severity and large-scaleevents that would be expected to kill essentially allmature trees across the areas larger than 100 ha wererare. However, precise size estimates of the largestpatches are probably limited by the sizes of our stands,which we expect based on the significant correlation ofmaximum disturbance patch sizes to stand sizes(Appendix S1: Fig. S6). The overall variation of patchsizes was likely adequately captured because we observedno correlation between all patch sizes and stand sizes.Natural disturbance characteristics markedly differ

from the prevailing management system of production

forests in the region (Fig. 2). The harvesting of all treeson 1–3 ha with a rotation period of approximately100 yr (Cardellini et al. 2018) is well outside the confi-dence interval of the spectrum of natural disturbancecharacteristics (Fig. 2). The logging operations underthe prevailing system have shorter rotation periods andtheir severities are extremely high, while the patch sizesand stand proportions disturbed tend to be relativelylow compared to the natural disturbance spectrum.The importance of moderate-severity, moderate-scale,

disturbance events for landscape dynamics is highlightedby the proportion of the total disturbed area representedby these disturbance types (Fig. 2). Altogether, 48% ofthe disturbed area was disturbed at moderate severity(25–75% canopy removal), which is comparable to the47% of the area disturbed by lower-severity events (10–25% canopy removal). The disturbance sizes were alsopredominantly of moderate scale. Patch sizes larger than10 ha comprised 58% of the total disturbed area, andstand proportions disturbed between 25% and 75% wereevident across 69% of the area. By contrast, small- andlarge-scale events represented just 22% and 9% of thearea considering the stand proportions disturbed. Thepatchy and irregular nature of disturbance events wassuggested by the higher prevalence of moderate eventswhen evaluated using stand proportion disturbed incomparison to the evaluation using disturbance severityor patch size. As is common in documented windstormsand bark-beetle outbreaks (Ips typographus), ourmethod depicts that individual disturbance events typi-cally produced multiple discrete disturbed patches perstand, where disturbance severity varied among thosepatches. Thus, a disturbance event was more likely char-acterized within the moderate disturbance categorywhen evaluated using the stand proportion disturbed atstand level, where all the discrete patches and plot-leveldisturbance severities were combined together.Spatial autocorrelation analysis supported that the

disturbance events were significantly spatially clustered

FIG. 2. Rotation periods (lines) of disturbance severities, patch sizes, and stand proportion disturbed showing the average (ide-alized) interval between consecutive events of similar or higher disturbance class. Notice that rotation periods of disturbance severi-ties (i.e., proportions of canopy area disturbed) are based on plot-level events, while periods of patch sizes and stand proportionsdisturbed are based on stands of an average size of approximately 50 ha. Gray bars represent a percentage of the area affected bythe disturbance events of specific severity, patch size, or stand proportion disturbed class. The locally prevailing production forestmanagement system of cutting all trees over the average area of 2 ha with an average rotation period of 100 yr is shown for compar-ison (black squares).

December 2020 QUANTIFYING FOREST DISTURBANCES Article e02189; page 7

(Fig. 3a), which indicated that plots closer to oneanother exhibited more similar years of disturbanceevents. The spatial autocorrelation in disturbance yearswas significant up to a distance of 739 m, which corre-sponds to a patch size of approximately 172 ha if thecorrelogram model intercept is considered to be repre-sentative of the patch radius. The synchrony of temporaltrends in patch sizes as estimated by two different meth-ods (i.e., for example, consistently peaking patch sizesaround 1820, 1870, 1920; Fig. 3b) supports the robust-ness of our approach. The larger patch size resultingfrom spatial autocorrelation analysis could be explainedby lower precision of this analysis. Similar years of dis-turbances does not necessarily mean the same events, asrequired in our patch-size analysis, and/or two closegroups of plots attributed to the same event separatedby plots with different events would be evaluated in thepatch-size analysis as two different patches, but theycould still contribute to the general autocorrelation (i.e.,the entire area within a given autocorrelation distancewas not necessarily disturbed in a given event). Theseresults likely indicate interesting clustering and syn-chrony beyond the pattern of single disturbance events.The MANOVA model (Fig. 4; Table 1) indicated that

disturbance severity was significantly different amongthe subregions. In contrast, stand proportion disturbedvaried significantly over time, as evident by the declineafter 1930 (i.e., during the last two 30-yr periods). Theeffects of subregion on patch sizes, and the interactionof subregion and time period on stand proportion dis-turbed was only marginally significant. In general, ourmodels explained only a small portion of the overallvariation in disturbance characteristics, which were con-siderably broad across the entire data set. The most dis-similar subregions differed in disturbance severity byjust 6.6%, and stand proportion disturbed decreased byabout 10% after about 1930 (Fig. 4). However, the over-all ranges that contained 95% values of disturbanceseverity, patch size, and stand proportion disturbed

values were as broad as 55%, 26 ha, and 71%, respec-tively. These subregional and temporal differencestogether with quantified overall variation provided amore robust and consistent picture of the confidenceranges of disturbance characteristics’ rotation periods(Fig. 2), suggesting that a temporally and spatiallynonequilibrium nature of forest dynamics is a typicalattribute.

DISCUSSION

We characterized, in detail, the disturbance regime ofmountain spruce forests of Central and Eastern Europeusing three complementary disturbance characteristics:disturbance severity, patch size, and stand proportiondisturbed. Continuous gradients from low- to high-severity and small to large disturbance events were char-acteristic of these forests. This pattern probably repre-sents well other temperate forests predominantlyaffected by wind and insect disturbances (Fraver et al.2009, Stueve et al. 2011, Nagel et al. 2017). High struc-tural complexity is evident across the landscape as aresult of these disturbance characteristics, particularly inareas that experience more intensive events, which canproduce spatially and temporally variable patterns ofdisturbance severity (Lehnert et al. 2013). Moderate-severity and moderate-scale events (tens of percent ofcanopy removed over tens of hectares) accounted for thelargest portion of the total disturbed area compared tosmall- and large-scale events, despite the fact that theywere less frequent than small-scale events.

Methodological improvements

Our approach improves upon traditional disturbancereconstruction methods by providing a framework toquantify distinct disturbance characteristics (disturbanceseverity, patch size, and stand proportion disturbed). Bycontinuously smoothing the disturbance proxy data

FIG. 3. (a) Moran’s I correlogram displaying trend of correlation of disturbance years in the plot neighborhood with increasingdistance. Black dots represent significant (P < 0.001) correlations suggesting that there is an association between disturbance yearsof neighboring plots up to a distance of several hundred meters. The correlogram model intercept shown by brown triangle (739 m)is used as a measure of distance up to which there is a spatial autocorrelation. (b) Temporal trend of Morans’ I correlogram modelintercepts (black dotted line) smoothed using a spline function (thick black line) in comparison to the trend of disturbance patchsizes also smoothed using a spline function (blue dashed line); the lines of independent measures of disturbance sizes show a similartrend with two peaks in late 19th and early 20th century and no clear long-term trend.

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using a kernel density function, we overcame the issue ofarbitrary decadal binning, which often leads to overesti-mation of the number of disturbance events and under-estimation their severity (Appendix S1: Fig. S7). The

method also provides an effective means to separate sig-nal from noise (i.e., the additional variability unrelatedto disturbances) in disturbance chronologies by accentu-ating synchronous evidence exceeding a defined thresh-old (Fraver and White 2005, Trotsiuk et al. 2018).The method allowed us to analyze the data at the level

of individual disturbance events, which is, for example,helpful for the analyses of disturbance synchronybecause in forests affected by a mixed-severity distur-bance regime, individual plots are often affected by sev-eral disturbance events, and each event can spandifferent subsets of plots (see an example inAppendix S1: Fig. S5). Previous approaches to evaluat-ing disturbance histories (e.g., as presented in Schurmanet al. 2018) estimated just aggregated decadal distur-bance rate, thus they could not identify whether trendsin disturbance chronologies resulted from changes in themean disturbance severity or changes in the number ofaffected plots. We demonstrated that the observeddecline in disturbance rates in our forests during the20th century (Schurman et al. 2018) was more stronglydriven by a decrease in the total area disturbed ratherthan a decrease in disturbance severity or patch size(Fig. 4). We believe that this and similar techniquescould extend the utility of dendrochronological methodsto estimate the historical range of variability of forestconditions, increase our understanding of moderate-severity disturbances, and provide valuable quantitativedata that are currently lacking for managers attemptingto emulate natural disturbance (Keane et al. 2009).

Comparison of past and contemporary disturbances

Our results can add to discussions concerning thepresence or the extent to which natural disturbanceshave intensified, especially in an effort to discover therelative effects of climate change. The maximum patchsizes that we estimated across our sites were similar tothe area-weighted mean patch sizes of contemporarynatural disturbance events in similar mountain forests ofCentral Europe (Harz in Germany, 58 ha, and the HighTatras in northern Slovakia, 60 ha) analysed using satel-lite imagery (Senf and Seidl 2018). However, the recentdisturbance in the Bohemian Forest mountain range wasmuch larger (3,550 ha; Senf and Seidl 2018). The largersize of the recent disturbance is likely a result of severalcoincident factors, including the large-scale

FIG. 4. Temporal trends in characteristics of disturbanceevents (dots), including plot-level severities (i.e., proportions ofcanopy areas disturbed), stand-level patch sizes, and stand propor-tion disturbed, smoothed using spline functions for each of the sixsubregions (colored lines) and for the entire region (black line).

TABLE 1. MANOVA model results showing the effects of subregion and time period on disturbance severity, patch size, and standproportion.

Effect

Subregion Period Subregion 9 PeriodMultiple

r2 PErrordfdf F r2 P df F r2 P Df F r2 P

Severity 5 4.41 0.07 <0.001 0.07 <0.001 295Patch size 5 2.16 0.04 0.059 0.04 0.059 295Standproportion

5 0.32 <0.01 0.901 5 3.92 0.06 0.002 24 1.58 0.12 0.045 0.18 0.009 266

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synchronization of forest ageing (in relation to the dis-turbance and management history), possibly combinedwith climate change-driven increases in sensitivity to dis-turbances (�Cada et al. 2016). However, our area-weighted mean patch size of 16.4 ha was lower than thatof contemporary disturbance events (Senf and Seidl2018), likely because our analysis was more sensitive tosmall-scale, low-severity disturbance (i.e., it includedevents affecting few trees covering small areas) over amuch longer history. Our results suggest that moderate-and small-scale events prevailed in the history of thestudy forests, while the rotation period of events killingall trees over areas larger than 100 ha was considerablylong. The potential of our data to study such large eventsis, however, limited (see Results and Appendix S1:Fig. S6). Generally, our approach provides the largelymissing, rigorous, and long-term data that should beused to put characteristics of current disturbance intocontext (Schurman et al. 2018).

Moderate disturbances and variation

Moderate-severity and moderate-scale disturbances,described as events of 25–75% severity, with 25–75%stand proportion disturbed, and patch sizes of 10–100 ha, were responsible for more than one-half of thetotal area disturbed across studied mountain spruce for-ests. They likely play a dominant role in many temperateand boreal forests because they affect a large aggregatearea, where they fundamentally modulate the foreststructure and its associated functions (Stueve et al.2011). Moderate disturbances are probably particularlyimportant in systems driven by wind and insect distur-bances (Fraver et al. 2009). Their impact is often patchyand spatially variable, even though they can affect largeareas (Kulakowski et al. 2017). As shown in our study, asingle disturbance event often creates several discretepatches (of irregular shape) within a stand, and theseverity can be highly variable within a single patch (seealso Nagel et al. 2017). Studies of contemporary moder-ate disturbances support these results and suggest thatthese events introduce heterogeneity at small, but alsomoderate stand scales, and they generate important liveand dead biological legacies, while usually maintainingstand resistance, resilience, biodiversity, and late-succes-sional species composition by favoring advanced regen-eration (Stueve et al. 2011, Meigs and Keeton 2018).The stand heterogeneity that developed as a result ofthese events and their frequent occurrence suggests thatmoderate disturbances are the key to the creation andmaintenance of forest structural complexity over timerather than a linear development path without any dis-turbance events (�Cada et al. 2016, Meigs et al. 2017).A nonrandom spatial distribution of disturbance

events seems to be a characteristic of moderate-scalewind and bark-beetle disturbances. The spatial autocor-relation may be partially attributable to higher intensityof these disturbances that result in the mortality of

larger groups of trees and disturbance patches. However,wind and bark-beetle (Ips typographus) disturbances aremodulated by tree and stand sensitivity, topography, andclimate (Thom et al. 2013); all of these factors can con-tribute to disturbance spatial autocorrelation. Further-more, individual disturbance events can generatefavorable conditions (such as exposed or injured trees)for subsequent disturbance events, so that consecutivedisturbance events are more likely to occur nearby(Thom et al. 2013). These relationships can synchronizestand dynamics over larger areas beyond the effects ofindividual events, as supported by our analysis of spatialautocorrelation. Synchronous aging of our study forestsresulting from past nonequilibrium dynamics wasrecently described by Schurman et al. (2018). The mag-nitude to which the forest types are predisposed tononequilibrium dynamics and synchronous agingbeyond the effects of individual disturbance events mayalso be related to site, climatic conditions, and distur-bance drivers. For example, exposed, high-altitudespruce forests driven by bark-beetle outbreaks can bemore sensitive to nonequilibrium dynamics, or, in otherwords, to higher temporal and spatial autocorrelation ofdisturbance events, than other forest types in Europe.We suggest that several levels of synchronization at dif-ferent temporal scales could be observed in forestdynamics. There is evidence of an annual scale synchro-nization driven, for example, by climatic factors (Senfand Seidl 2018), but also evidence of decadal or centen-nial scales of synchronization that could be driven insome forest types, for example, by aging (Schurmanet al. 2018).The variation of disturbance characteristics is

expected to be related to disturbance drivers, topogra-phy, site conditions, or geographical position, which arethe factors that vary regionally. Our study forests areexpected to be exposed to increased frequency or size ofdisturbances because of exposed topographic positionsat high elevations or because of insect disturbances (Fra-ver et al. 2009), which are moreover often coupled withwindthrow events (Senf and Seidl 2018). In contrast,steep slopes, complex topography, and poor site condi-tions of most of our study sites can contribute to morelimited disturbance severity or size compared to spruceforests on flatter terrain or mountain ridges (Trotsiuket al. 2014, �Cada et al. 2016, Senf and Seidl 2018). Nev-ertheless, the variation in disturbance characteristicsacross subregions and over time, as explained by ourmodels, were not notably beyond the general variationof disturbance characteristics. This finding implies thatthe region-wide trends are generally applicable until amore detailed understanding of the relationship betweendisturbance characteristics and environmental condi-tions that affect them more directly is available. We sug-gest that the nonequilibrium nature of forestdisturbances causing meaningful temporal and spatialvariation should be considered during managementapplication and mean trends of rotation periods of the

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disturbance characteristics (Fig. 2) should be coupledwith an understanding of their range of variation. Suchan approach also allows for some flexibility for decisionmakers to use a range of options to achieve the desiredecosystem services.

Management implications

Current forest management approaches in Centraland Eastern European forests typically generate a muchnarrower range of structural variation than occurs innatural forests. Using homogeneous techniques, currentapproaches, for example, create either a combination ofhighly opened and shaded light conditions in even-agedsystems, or homogeneously shaded conditions producedwhen selection cuttings are applied under continuouscover forestry systems. These conditions differ from thecomplex light conditions observed in natural forestsafter moderate-severity disturbances (Meigs and Keeton2018). Our results support a similar conclusion byrevealing that current management treatments tend tobe extremely severe and relatively small, and their rota-tion periods are much shorter compared to the naturaldisturbance spectrum. Thus, current management prac-tices are, in many important ways, outside the range ofvariation observed in natural forests (Fig. 2). Shorterrotation periods in managed forests clearly shift age dis-tributions towards younger forests, thus the presence ofold trees and related late developmental structures aretypically absent in managed forests. Prevailing manage-ment often fails to consider the biological legacies ofnatural disturbance and tree senescence (Meigs and Kee-ton 2018), thus the related structures, such as dead anddying trees or lying deadwood, are missing. Our quanti-tative results could be used to guide efforts to emulatenatural disturbance patterns in logging operations(Keane et al. 2009, Long 2009) and to provide moreexact recommendations for retention forestryapproaches (Fedrowitz et al. 2014, Mori and Kitagawa2014).Ecological benefits would be maximized where forest

management acknowledges the broad spectrum of natu-ral disturbance effects and their biological legacies.Heterogeneous treatments should be preferred to mimicthe observed spectrum of low- to moderate-severity andsmall- to moderate-size disturbances, and they should bedistributed spatially and temporally in accordance withnatural patterns to increase landscape connectivity andforest resilience. The severity of treatments should be sig-nificantly lower than 100%. Some portion of the treesand important structural attributes should be retained inthe stand perpetually to facilitate key structural elements(including deadwood) and stand resilience. The level ofretention should consider the need for structural legacyamounts, the cumulative effects of natural and humandisturbances, landscape structure resulting from the pre-vailing management system, and historical disturbancerates. For European mountain forests, based on the

ninety-eighth percentile of historical natural disturbanceseverities, we recommend that the amount of retainedtrees would represent at least 30% of the canopy area.Combining appropriate levels of retention with spatiallyirregular treatment shapes and severities could, in accor-dance with natural disturbance patch sizes, enable theapplication of treatments to larger, moderate-scale areasthan are typically considered under current approaches(Meigs and Keeton 2018). The eighty-seventh percentileof our patch sizes would, for example, correspond to themaximum treatment size of about 15 ha. Retentionshould include patches without any logging operationsand some degree of natural disturbances should also beaccepted in managed areas to maintain the associatedlegacy structures and promote their accumulation acrossthe landscape. The probable natural mortality of a por-tion of the retained trees would be consistent with thepositive effects of natural disturbances on biodiversity(Lehnert et al. 2013).Our estimations of disturbance characteristics could

also be used to help design forest conservation areas thatare key for maintaining diversity. These areas should belarge enough and adequately connected to guaranteeforest resilience to disturbances. Effective conservationrequires protection of a “minimum dynamic area” thatcan be estimated, for instance, using the characteristicsof natural disturbances; the protected area should beconsiderably larger than the largest disturbance patchand include different tree age cohorts (Pickett andThompson 1978). Based on this suggestion, our analysessupport a minimum reserve area on the order of hun-dreds of hectares. However, where stands are geographi-cally isolated (far from other reserves or surrounded bya hostile environment) or structurally homogeneous (interms of tree age structure), much larger reserves (possi-bly larger than 10,000 ha) could be required to guaran-tee viable populations of species under the moderate-scale, mixed-severity disturbance regime (Lehnert et al.2013, Mikol�a�s et al. 2017a).

CONCLUSIONS

We tested a comprehensive method of estimating pastdisturbance severity, patch size, and stand proportiondisturbed using a dendrochronological reconstructionacross an extensive plot network in remaining fragmentsof European primary forests; this approach can providevaluable baseline data for ecological research and con-temporary forest landscape management. Our evalua-tion emphasized the significance of moderate-scale,moderate-severity, disturbance events for dynamics andmanagement of temperate forests, particularly those dri-ven by wind and insect disturbances. Moderate distur-bances, often overlooked in past research, wereespecially common when comparing the total area theyaffected to that of small- and large-scale events. Naturaldisturbances should be considered a significant processthat creates conditions consistent with native species

December 2020 QUANTIFYING FOREST DISTURBANCES Article e02189; page 11

habitat requirements. Therefore, to sustain biodiversityand ecosystem functions, forest management efforts(strict reserves network and the sustainable forest man-agement elsewhere) should be based on the historicalrange of forest variability (e.g., the variation of distur-bance severities and sizes) and key structural elements(e.g., patches of living trees, advanced regeneration, deadstanding trees, lying deadwood) should be maintained.Our quantitative estimates of disturbance characteristicsprovide rigorous data to guide such efforts.

ACKNOWLEDGMENTS

We thank the numerous local experts and managers for theirhelp and permissions with the field work. We thank the numer-ous technicians that helped to collect the data. Input fromanonymous reviewers greatly improved the manuscript. Fund-ing for this research was provided by the Czech Science Founda-tion (Grant GACR no. 16-23183Y) and the Czech Ministry ofEducation, Youth and Sports (project Inter-Transfer no.LTT20016 and project EVA4.0 no. CZ.02.1.01/0.0/0.0/16_019/0000803).

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SUPPORTING INFORMATION

Additional supporting information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/eap.2189/full

DATA AVAILABILITY

Data are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.08kprr507

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