Article Climate-Induced Northerly Expansion of Siberian Silkmoth Range

Viacheslav I. Kharuk 1,2,* ID , Sergei T. Im 1,3,4, Kenneth J. Ranson 5 and Mikhail N. Yagunov 6

1 V.N. Sukachev Institute of Forest SB RAS, Federal Research Center Krasnoyarsk Science Center SB RAS Academgorodok 50/28, 660036 Krasnoyarsk, Russia; [email protected] 2 Siberian Federal University, Institute of Space and Information Technology, pr. Kirenskogo 26a, 660074 Krasnoyarsk, Russia 3 Siberian Federal University, Institute of Ecology and Geography, pr. Svobodny 79, 660041 Krasnoyarsk, Russia 4 Reshetnev Siberian State University of Science and Technology, Institute of Space Research and High Technologies, pr. Krasnoyarskiy Rabochii 31, 660014 Krasnoyarsk, Russia 5 NASA s Goddard Space Flight Center, Greenbelt, MD 20771, USA; [email protected] 6 Russian Center of Forest Protection, 660036 Krasnoyarsk, Russia; [email protected] * Correspondence: [email protected]

Received: 15 June 2017; Accepted: 10 August 2017; Published: 16 August 2017

Abstract: Siberian silkmoth ( sibiricus Tschetv.) is a dangerous pest that has affected nearly 2.5 × 106 ha of “dark taiga” stands (composed of Abies sibirica, Pinus sibirica and Picea obovata) within the latitude range of 52◦–59◦ N. Here we describe a current silkmoth outbreak that is occurring about half degree northward of its formerly documented outbreak range. This outbreak has covered an area of about 800 thousand ha with mortality of conifer stands within an area of about 300 thousand ha. The primary outbreak originated in the year 2014 within stands located on gentle relatively dry southwest slopes at elevations up to 200 m above sea level (a.s.l.) Then the outbreak spread to the mesic areas including northern slopes and the low-elevation forest belts along the Yenisei ridge. Within the outbreak area, the northern Siberian silkmoth population has reduced generation length from two to one year. Our study showed that the outbreak was promoted by droughts in prior years, an increase of the sum of daily temperatures (t > +10 ◦C), and a decrease in ground cover moisture. Within the outbreak area, secondary pests were also active, including the aggressive Polygraphus proximus bark borer beetle. The outbreak considered here is part of the wide-spread (panzonal) Siberian silkmoth outbreak that originated during 2014–2015 with a range of up to 1000 km in southern Siberia. Our work concludes that observed climate warming opens opportunities for Siberian silkmoth migration into historically outbreak free northern “dark taiga” stands.

Keywords: Siberian silkmoth; climate change; phyllophages; pest outbreaks; biotic impact on forests; Siberian taiga; climate impact on

1. Introduction Siberian silkmoth (Dendrolimus sibiricus Tschetv.) outbreaks were historically observed within the range of 52◦ N and ≈59◦ N latitude [1]. The maximal known outbreak area reached 2.5 × 106 ha (1953–57 year, Figure1)[ 2]. Preferred food tree species for Siberian silkmoth are fir (Abies sibirica) and Siberian (Pinus sibirica), but the also feeds on spruce (Picea obovata) and larch (Larix sp.). It’s considered that pest outbreaks are induced by droughts, low precipitation and increased air temperatures at the beginning of vegetative period [3]. The northern boundary of outbreaks was approximated by the sum of temperature (t > +10 ◦C) equal to 1200–1400 ◦C by Rojkov [4]. The Siberian silkmoth outbreak cycle includes a beginning phase, prodromal, eruption (or the outbreak

Forests 2017, 8, 301; doi:10.3390/f8080301 www.mdpi.com/journal/forests Forests 2017, 8, 301 2 of 9 itself), and decreasing phases. Mean eruption and decreasing phases length are about three and two years, respectively. The entire outbreak length is about 10 years when about seven pest generations have occurred, including at least two generations with a one-year generation cycle. In the beginning phase caterpillar density is less than one caterpillar per tree, whereas in the eruptive phase it has been reported to reach 20,000/tree [3,5]. Climate change may affect population dynamics of different pest species, may increase outbreak frequency and may facilitate the shift of range to more northerly latitudes and higher elevations [6–8]. For example, northern expansion of the eastern spruce budworm, Choristoneura fumiferana in eastern North America may permit greater defoliation and mortality in extensive northern black spruce forests [9]. Douglas-fir tussock is expanding in the southwestern United States and into northern Mexico [10]. Gypsy moth, Lymantria dispar is predicted to increase its range in Canada [11]. Droughts and increased aridity are considered to be responsible for the catastrophic outbreaks of Dendroctonus ponderosae in North American forests [12]. Periodic droughts stimulated Dendrolimus pini outbreaks in Germany forests [13]. Water stress weakened fir (A. sibirica) in Central Siberia, which lead to a huge outbreak of bark beetle Polygraphus proximus and, as a consequence, widespread fir mortality [14]. Expansion of the northward range of Dendrolimus sibiricus, also in Siberia, was reported by Kharuk et al. [15]. Siberian forests exist within a global “hot spot” of increasing air temperature that has persisted over the past several decades [16]. As a result, insect populations may experience noticeable changes. The goal of this paper is to describe an analysis of causes and dynamics of an on-going Dendrolimus sibiricus outbreak occurring northward of the known pest species range. We were seeking to answers the following questions: (i) What is the chronology of Siberian silkmoth outbreak? (ii) What is the relationship between outbreak occurrence and climate variables? (iii) What are the potential changes to the geographical range of Siberian silkmoth outbreaks?

2. Materials and Methods

2.1. Study Area The majority of silkmoth outbreak areas occurred within southern part of the Yenisei and Ob Rivers watersheds. Outbreaks occurred within other regions of Central and Eastern Siberia almost simultaneously with the Yenisei outbreak, as shown in Figure1. Within the Yenisei outbreak area mean annual precipitation is 540 mm (190 mm in summer); mean January and July temperatures are −21 ◦C and +19 ◦C, respectively. The study forests are moss, grass-moss, and shrub-moss types. Stands composed of fir and Siberian pine (dominant species) with presence of spruce (Picea obovata), birch () and aspen (Populus tremula); the proportion of the latter three species in canopy was about 10–30%. Siberian pine mean age, height and diameter are 213 years, 21 m and 35 cm, respectively. Fir mean age, height and diameter are 95 years, 17 m and 18 cm, respectively.

2.2. Data Satellite (i.e., Landsat 7 and 8, Sentinel-2A, Worldview) and ground-truth data of the Yenisei outbreak area were analyzed to identify local outbreak areas and assist in the analysis of outbreak site characteristics. Landsat digital data were obtained from the United States Geological Survey (USGS) GloVis web-site (http://glovis.usgs.gov). Sentinel-2A scenes (described by https://scihub. copernicus.eu/dhus) with spatial resolution 10 m (blue, green, red, and NIR) were used. High-res (0.5 m) Worldview scenes were obtained from https://www.bing.com/mapspreview and https:// www.google.com/maps and were used primarily as visual references of local site conditions. A total of fifteen Landsat and two Sentinel-2A scenes were analyzed spanning the years 2013–2016. Forests 2017, 8, 301 3 of 9 Forests 2017, 8, 301 3 of 9

FigureFigure 1. Location 1. Location (rectangle) (rectangle) ofthe of the current current (“Yenisei”) (“Yenisei”) Siberian Siberian silkmoth outbreak. outbreak. White White triangles: triangles: locationlocation of earlier of earlier observed observed pest pest outbreaks; outbreaks; black black triangles: triangles: outbreaks outbreaks thatthat occurredoccurred in in 2014–2015. 2014–2015. 1, 2—largest1, 2—largest outbreaks: outbreaks: “Ket-Chulym” “Ket-Chulym” (1951–1957) (1951–1957) and “Priangar’e” and “Priangar’e” (1994–1996), (1994–1996), respectively; respectively; 3—“dark needle3—“dark conifer taiga”,needle conifer composed taiga”, of Abiescomposed sibirica of Abies, Pinus sibirica sibirica, Pinusand sibiricaPicea obovataand Picea(according obovata (according to the map of Bartalevto the et map al. [of17 Bartalev]). Inset et (right): al. [17]). 1—enlargement Inset (right): 1—enlargement showing the northernshowing the boundary northern of boundary “Ket-Chulym” of outbreak“Ket-Chulym” (former most outbreak northward (former known most pestnorthward outbreak); known 4—dead pest outbreak); stands within 4—dead the stands “Yenisei” within outbreak the area. Initial“Yenisei” (primary) outbreak Yenisei area. Initial outbreak (primary) locations Yenise arei outbreak shown bylocations stars. Arroware shown indicates by stars. the Arrow distance betweenindicates former the northern distance outbreak between boundaryformer northern andnorthern outbreak boundaryboundary and of the northern “Yenisei” boundary outbreak. of the Inset “Yenisei” outbreak. Inset (left): time series of dead stands area dynamics. (left): time series of dead stands area dynamics. 2.3. Image Analysis 2.3. Image Analysis ESRI ArcGIS (http://www.esri.com) and Erdas Imagine (http://geospatial.intergraph.com) ESRIsoftware ArcGIS were (usedhttp://www.esri.com for image analysis. )Images and Erdas were mosaicked Imagine (andhttp://geospatial.intergraph.com projected onto UTM (zone 46) ) softwarereference. were usedA mask for of imagedark needle analysis. stands Images acquired were priormosaicked to the outbreak and (2013) projected was generated onto UTM based (zone 46) reference.on expert A maskknowledge of dark and needle a supervised stands acquired classification prior toof theLandsat outbreak and (2013)Worldview wasgenerated scenes. The based on expertmaximum knowledge likelihood and aclassification supervised method classification with a threshold of Landsat procedure and Worldview (threshold scenes. p = 0.05) The was maximum used likelihoodto generate classification classification method map with of “dark a threshold needle procedurestands” (composed (threshold by pA.= sibirica 0.05) was, P. sibirica used to and generate P. obovata). In total, fourteen representative samples of dark needle stands were selected (about 13,700 classification map of “dark needle stands” (composed by A. sibirica, P. sibirica and P. obovata). In total, pixels per sample). The mean Jeffries-Matusita distance between samples was 1040 (confusion error fourteen representative samples of dark needle stands were selected (about 13,700 pixels per sample). ≈23%). Area of the dark needle stands was 18,250 km2 (≈18.5% of the analyzed area). Classification The meanmaps Jeffries-Matusita of dead and dying distance stands (with between >75% and samples >50% wasof dead 1040 trees, (confusion respectively) error were≈23%). generated Area for of the 2 dark needlethe years stands 2014–2016. was 18,250 km (≈18.5% of the analyzed area). Classification maps of dead and dying standsThe (with SRTMGL1 >75% digital and >50% elevation of dead model trees, (horizontal respectively) spatial resolution were generated 30 m, vertical for the 8.6 years m; available 2014–2016. Theat SRTMGL1(https://lpdaac.usgs.gov/dataset_discovery/me digital elevation model (horizontalasures/measures_products_table/srtmgl1v003) spatial resolution 30 m, vertical 8.6 m; availablewas at (https://lpdaac.usgs.gov/dataset_discovery/measures/measures_products_table/srtmgl1v003used to analyze the spatial distribution of stands (i.e., with respect to elevation, exposure and slope ) was usedsteepness). to analyze First, the topograp spatialhic distribution normalization of stands was applied (i.e., with to all respect scenes tobased elevation, on the C-algorithm exposure and [18]. slope steepness).Topographic First, topographic features (elevation, normalization slope steepness, was applied aspect) were to all analyzed scenes based with di onscretization the C-algorithm of 100 m, [18]. 5°, and 45°, respectively. Topographic features (elevation, slope steepness, aspect) were analyzed with discretization of 100 m, 5◦, and 45◦, respectively. Ground-truth data were collected by specialists from the Forest Defense Center of Krasnoyarsk Region. Data included species composition, canopy closure, dominant species, age, diameter at breast Forests 2017, 8, 301 4 of 9 height (dbh = 1.3 m), height, and tree vigor, regeneration and soil type. The climate and ecological variablesForests used2017, 8, in 301 the analysis of pest outbreak occurrence are presented in Table1. 4 of 9

Ground-truth data were collected by specialists from the Forest Defense Center of Krasnoyarsk Table 1. Variables and datasources. Region. Data included species composition, canopy closure, dominant species, age, diameter at breast height (dbh = 1.3 m), height, and tree vigor, regeneration and soil type. The climate and ecological Dataset Spatial Time N Data Source Variables Used variablesName used in the analysis of pest outbreak occurrenceResolution are presentedCoverage in Table 1. Taseevo Temperature, precipitation 1 http://meteo.ru Point 1988–2016 meteo station Table 1. Variables and datasources. (distance from outbreak ~50 km) Yeniseisk Spatial Time Temperature, precipitation 2 N Dataset Name http://climexp.knmi.nl Data Source Point 1871–2016 Variables Used meteo station Resolution Coverage (distance from outbreak ~90 km) Taseevo meteo https://crudata.uea.ac.uk/cru/ Temperature, precipitation 31 CRU TS 3.23 http://meteo.ru Point0.5 ◦ × 0.5◦1988–20161901–2014 Temperature, precipitation station data/hrg/cru_ts_3.23 (distance from outbreak ~50 km) Yeniseisk Temperature, precipitation 2 GHCN https://www.esrl.noaa.gov/psd/htpp://climexp.knmi.nl Point 1871–2016 4 meteo station 0.5◦ × 0.5◦ 1948–2016(distance from Temperatureoutbreak ~90 km) CAMS data/gridded/data.ghcncams.html https://crudata.uea.ac.uk/cru/data/ 3 CRU TS 3.23 https://www.esrl.noaa.gov/psd/ 0.5° × 0.5° 1901–2014 Temperature, precipitation 5 GPCC hrg/cru_ts_3.23 0.5◦ × 0.5◦ 2013–2016 Precipitation https://www.esrl.noaa.gov/psd/dadata/gridded/data.gpcc.html 4 GHCN CAMS 0.5° × 0.5° 1948–2016 Temperature MERRA2 ta/gridded/data.ghcncams.html ftp://goldsmr4.sci.gsfc.nasa.gov/ Daily temperature Top soil 6 M2SDNXSLV. https://www.esrl.noaa.gov/psd/da 0.5◦ × 0.625◦ 1980–2016 5 GPCC /data/s4pa/MERRA2_MONTHLY 0.5° × 0.5° 2013–2016 Precipitation(0–2 cm) moisture 5.12.4 ta/gridded/data.gpcc.html MERRA2 ◦ ◦ 7 SPEI ftp://goldsmr4.sci.gsfc.nasa.gov//dhttp://sac.csic.es/spei 0.5 × 0.5 1950–2016 Daily temperatureDrought Top index soil 6 M2SDNXSLV. 0.5° × 0.625° 1980–2016 ata/s4pa/MERRA2_MONTHLY (0–2 cm) moisture 5.12.4 7Drought SPEI was analyzed http://sac.csic.es/spei based on SPEI (The Standardized 0.5° × 0.5° 1950–2016 Precipitation-Evapotranspiration Drought index Index) data which were obtained from http://sac.csic.es/spei (spatial resolution 0.5◦ × 0.5◦). SPEI is based Drought was analyzed based on SPEI (The Standardized Precipitation-Evapotranspiration on the difference (D ) between precipitation amount (P ) and potential evapotranspiration (PET ), Index) data whichi were obtained from http://sac.csic.es/speii (spatial resolution 0.5° × 0.5°). SPEI is i where i—period, data are normalized in space and time [19]: based on the difference (Di) between precipitation amount (Pi) and potential evapotranspiration (PETi), where i—period, data are normalized in space and time [19]: Di = Pi − PETi Di = Pi – PETi 3. Results and Discussion 3. Results and Discussion 3.1. Siberian Silkmoth Outbreak Chronology in Central Siberia 3.1. Siberian Silkmoth Outbreak Chronology in Central Siberia Recorded pest outbreaks within Central Siberia have covered an area up to 1.0 × 106–2.5 × 106 ha. Recorded pest outbreaks within Central Siberia have covered an area up to 1.0 × 106–2.5 × 106 ha. Since the catastrophic outbreak in 1951–1957 (2.5 × 104 km2), outbreak area has decreased (Figure2). Since the catastrophic outbreak in 1951–1957 (2.5 × 104 km2), outbreak area has decreased (Figure 2). This canThis becan attributed be attributed to ato reductiona reduction in in the the SiberianSiberian silkmoth silkmoth “food “food base” base” caused caused by earlier by earlier outbreaks, outbreaks, as wellas well as a as decrease a decrease in forestin forest stand stand size size andand increased fragmentation fragmentation due due to logging. to logging. The “Yenisei” The “Yenisei” outbreakoutbreak discussed discussed here here has has already already affected affected intact stands stands northward northward of ofthe the historical historical latitudinal latitudinal boundaryboundary of Siberian of Siberian silkmoth silkmoth outbreaks outbreaks (Figure(Figure 11)).. Within the the Yenisei Yenisei outbreak outbreak area area silkmoth silkmoth have have reducedreduced generation generation length length from from two two years years toto one ye year,ar, a afact fact that that is isnot not trivial trivial nor norobvious obvious for the for the northernnorthern Siberian Siberian silkmoth silkmoth population. population.

100

2 R² = 0.76 10 km 3 1

0.1 Area, × 10

0.01 1870 1890 1910 1930 1950 1970 1990 2010 Year

FigureFigure 2. Siberian 2. Siberian silkmoth silkmoth outbreak outbreak chronology chronology (acco (accordingrding to to [2,3,14,20–22]). [2,3,14,20–22 Trend]). Trend shown shown without without latestlatest outbreak outbreak (which (which is shownis shown by by light light gray gray mark).mark).

Forests 2017, 8, 301 5 of 9

Forests 2017, 8, 301 5 of 9 3.2. Outbreak Relevance to Relief Features 3.2. Outbreak Relevance to Relief Features Forests 2017, 8, 301 5 of 9 The initial Yenisei Silkmoth outbreak was discovered in 2014. Later topographic analysis showed The initial Yenisei Silkmoth outbreak was discovered in 2014. Later topographic analysis that the outbreak site was in an area of gentle (up to 5◦) slopes of south-west exposure with elevation showed3.2. Outbreak that the Relevance outbreak to site Relief was Features in an area of gentle (up to 5°) slopes of south-west exposure with about 150–180 m (Figure3a). Within two years forest damage spread to elevations up to 350 m mostly elevationThe about initial 150–180 Yenisei m (FigureSilkmoth 3a). outbreak Within twowas yearsdiscovered forest damagein 2014. spread Later totopographic elevations analysisup to on slopes of northern exposure with steepness up to 25◦ (Figure3b,c). A ten-fold increase in area 350showed m mostly that onthe slopes outbreak of northernsite was inexposure an area ofwith gent steepnessle (up to 5°)up slopesto 25° of(Figure south-west 3b,c). exposure A ten-fold with 2 2 occurredincreaseelevation between in area about occurred July 150–180 2014 between m and (Figure July July 3a). 20152014 Within (and≈1–10 July two km2015 years) ( and≈ 1–10forest another km damage2) and tenfold anotherspread increase totenfold elevations (increase≈10–100 up to km ) during(≈10–100350 Julym kmmostly of2) during 2015. on slopes July By theof of2015. yearnorthern By 2017 the exposureyear total 2017 outbreak totawithl outbreaksteepness area reached area up reachedto 25° about (Figure about 800,000 800,000 3b,c). ha Aha withten-fold with conifer mortalityconiferincrease mortality within in area about within occurred 300 about thousand between 300 thousand July ha with2014 ha andawith trend July a trend of2015 increasing of (≈ increasing1–10 km area2) andarea of another damagedof damaged tenfold stands. stands. increase (≈10–100 km2) during July of 2015. By the year 2017 total outbreak area reached about 800,000 ha with conifer60 mortality within about 300 thousand ha withN a trend of increasing40 area of damaged stands. a 1 b 20 1 1 35 c 2 2 NW NE 2 30 40 3 60 3 10 40 3 N 25 a 1 b 20 1 1 35 c 2 20 Area, % 2 W NW 0 NEE 2 20 3 % Area, 30 40 3 10 15 3 25 10 20 0Area, % SWW 0 SE E 5

20 % Area, 60 90 120 150 180 210 240 270 15 0 30 60 90 120 150 180 210 240 S 010 5 10 15 20 Elevation above sea level, m Slope steepness, degrees 0 SW SE 5 60 90 120 150 180 210 240 270 0 30 60 90 120 150 180 210 240 S 0 5 10 15 20 FigureFigure 3. 3. Distribution of dead and of outbreak primary locus stands within outbreak area with respect DistributionElevation above sea of level, dead m and of outbreak primary locus stands within outbreakSlope area steepness, with degrees respect toto (a )(а elevation;) elevation;( b(b)) azimuth;azimuth; and and (c (c) )slope slope steepness steepness (medians (medians shown shown by vertical by vertical lines). lines).1—outbreak 1—outbreak primaryprimaryFigure locus; locus; 3. Distribution 2—all 2—all dead of stands;stands; dead and 3—all 3—all of outbreakanalyzed analyzed primaryarea area.. Data locus Data normalized stands normalized within with respect withoutbreak respect to % area of to allwith% stands.of respect all stands. to (а) elevation; (b) azimuth; and (c) slope steepness (medians shown by vertical lines). 1—outbreak 3.3. Pest Outbreak and Climate Variables 3.3. Pest Outbreakprimary locus; and 2—all Climate dead Variables stands; 3—all analyzed area. Data normalized with respect to % of all stands. Within the study area, significant changes were observed for drought index SPEI, sum of positive (t >Within3.3. 0 °C) Pest temperatures theOutbreak study and area,and Climate the significant number Variables of changesdays with were positive observed temperatures for drought (Figure 4). index Since SPEI, 2000, sumthe latter of positive ◦ (t >two 0 variablesC)Within temperatures increased the study to andarea, +120 thesignificant °C number and +9 changes days, of days resp wereectively with observed positive (reference for drought temperatures period: index 1980–1999) SPEI, (Figure sum (Figure4 of). positive Since 4b). 2000, ◦ theAlong latter(t > 0 with °C) two temperaturesthat, variables drought increased andseverity the number and to frequenc +120 of daysCy andincreasedwith +9positive days, (years temperatures respectively 2003, 2006, (Figure2012; (reference Figure 4). Since 4a). period: 2000, the 1980–1999) latter (Figuretwo4 b).variables Along increased with that, to +120 drought °C and +9 severity days, resp andectively frequency (reference increased period: 1980–1999) (years 2003, (Figure 2006, 4b). 2012; FigureAlong4a). with that, drought severity and frequency increased (years 2003, 2006, 2012; Figure 4a).

Figure 4. Climate variables dynamics within the zone of Yenisei pest outbreak. (а) Standardized Precipitation-Evapotranspiration Index (SPEI, which indicated drought years); (b) sum of t > 0 °C and FigurenumberFigure 4. ofClimate 4.days Climate with variables tvariables > 0 °C. dynamics dynamics withinwithin the zone zone of of Yenisei Yenisei pest pest outbreak. outbreak. (а) Standardized (a) Standardized Precipitation-EvapotranspirationPrecipitation-Evapotranspiration Index Index (SPEI, (SPEI, which which indicated drought drought years); years); (b) ( bsum) sum of t of> 0t °C> 0and◦C and Climatic time series are presented for areas where earlier major outbreaks occurred (Ket-Chulym numbernumber of days of days with witht > t 0 >◦ 0C. °C. and Priangar’e) within the historical outbreak range of the insect, and for the current Yenisei outbreak (Figure Climatic5). Outbreaks time series were generallyare presented preceded for areas by anwh inerecrease earlier in majorthe sum outbreaks of positive occurred (Figure (Ket-Chulym 5g,l) and of ClimaticMay–Juneand Priangar’e) time air temperatures serieswithin are the presentedhistorical (Figure 5a,d,i),outbreak for areas as rangewell where as of by the earlier a insect,decrease major and in for outbreaksprecipitation the current occurred (FigureYenisei 5b,e,j),outbreak (Ket-Chulym andan Priangar’e) (Figureincrease 5). in Outbreaks the within drought the were historicalindex generally (Figure outbreak preceded 5c,f,k), range andby an aof indecreasecrease the insect, in in the top-soil and sum for of moisture positive the current (Figure(Figure Yenisei 5h,m).5g,l) outbreakand (FigureNotably,of 5May–June). Outbreaksthose outbreaks air temperatures were were generally preceded (Figure preceded 5a,d,i),or coincide as by well and with increaseas by drought a decrease in theyears. sumin precipitationThis of pattern positive was(Figure (Figure true 5b,e,j), for5g,l) and ofall May–June anoutbreaks increase air that in temperatures the occurred drought in theindex (Figure period (Figure5 a,d,i),1950–2015. 5c,f,k), as well and asa decrease by a decrease in top-soil in precipitation moisture (Figure (Figure 5h,m).5b,e,j), an increaseNotably, in those the droughtoutbreaks indexwere preceded (Figure5 orc,f,k), coincide andd awith decrease drought in years. top-soil This moisture pattern was (Figure true for5h,m). Notably, all outbreaks those outbreaks that occurred were in preceded the period or 1950–2015. coincided with drought years. This pattern was true for all outbreaks that occurred in the period 1950–2015.

Forests 2017, 8, 301 6 of 9 Forests 2017, 8, 301 6 of 9

Ket-Chulym Priangar'e Yenisei 25 25 25 a d i 20 20 20

15 15 15

10 10 10 Temperature, °C Temperature, °C Temperature, Temperature, °C 5 May 5 May 5 May June June June July July July 0 0 0 1940 1950 1960 1970 1980 1990 2000 2010 2000 2005 2010 2015 2020 Year Year Year 200 200 200 b e j May 150 June 150 May 150 May July June June July July

100 100 100 Precipitation, mm Precipitation, 50 mm Precipitation, 50 mm Precipitation, 50

0 0 0 1940 1950 1960 1970 1980 1990 2000 2010 2000 2005 2010 2015 2020 Year Year Year 3 3 3 c f k 2 2 2

1 1 1

0 0 0

-1 -1 -1

May units relative SPEI, units relative SPEI, May May SPEI, relative units relative SPEI, June -2 -2 June -2 June July July July -3 -3 -3 1940 1950 1960 1970 1980 1990 2000 2010 2000 2005 2010 2015 Year Year Year 2,600 130 2,600 120 g l 120 110 2,200 2,200 110 100 100 1,800 1,800 90 90 80 Number of days Number of 80 days Number of 1,400 1,400 Sum of temperature, °C temperature, of Sum Σ(t > 10 °C) °C temperature, of Sume Σ(t > 10 °C) 70 70 N(t > 10 °C) N(t > 10 °C) 1,000 60 1,000 60 1980 1990 2000 2010 2000 2005 2010 2015 Year Year 1.0 1.0 h m 0.9 0.9

0.8 0.8

0.7 0.7

0.6 0.6

May May 0.5 0.5 June June July July Top soil (0-2Top cm) wetness, fraction Top soil (0-2Top cm) wetness, fraction 0.4 0.4 1980 1990 2000 2010 2000 2005 2010 2015 Year Year

FigureFigure 5. Climate 5. Climate variables variablesdynamics dynamics before before and and during during “Ket-Chulym” “Ket-Chulym” (a–c); “Priangar’e” (a–c); “Priangar’e” (d–h) and ( d–h) “Yenisei” (i–m) pest outbreaks. Vertical lines indicated beginning and end of outbreak. (a,d,i)— and “Yenisei” (i–m) pest outbreaks. Vertical lines indicated beginning and end of outbreak. temperature; (b,e,j)—precipitation; (c,f,k)—SPEI; (g,l)—sum of temperature; (h,m)—top soil wetness. (a,d,i)—temperature; (b,e,j)—precipitation; (c,f,k)—SPEI; (g,l)—sum of temperature; (h,m)—top soil wetness.

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3.4. Northern Boundary of Siberian Silkmoth Range 3.4. Northern Boundary of Siberian Silkmoth Range According to empirical observations [4], the historical northern boundary of the Siberian According to empirical observations [4], the historical northern boundary of the Siberian silkmoth silkmoth range was approximated by the geographical limit of cumulative temperatures above 10 °С range was approximated by the geographical limit of cumulative temperatures above 10 ◦C exceeding exceeding 1200 °С, whereas the southern boundary was approximated at summation exceeding 1200 ◦C, whereas the southern boundary was approximated at summation exceeding 2200 ◦C. The 2200 °С. The insect climatic envelope is presented in Figure 6, based on the air temperature data of insect climatic envelope is presented in Figure6, based on the air temperature data of the 1960s. As the 1960s. As it can be seen in Figure 6, the 21st century temperature northern limit shifted northward it can be seen in Figure6, the 21st century temperature northern limit shifted northward by about by about 150–500 km. The Yenisei outbreak area can be observed mostly above the historical climatic 150–500 km. The Yenisei outbreak area can be observed mostly above the historical climatic envelope envelope of the Siberian silkmoth. of the Siberian silkmoth.

◦ Figure 6. Sum of daily temperatures t > 10 C(Σt>+10◦C, 2011–2016 mean). 1—the current Yenisei outbreakFigure 6. (white-dashed Sum of daily line).temperatures 2—historical t > 10 Siberian °C (Ʃt>+10°C silkmoth, 2011–2016 climatic mean). envelope 1—the [4 ],current modified Yenisei by Baranchikovoutbreak (white-dashed and Kondakov line). (http://forest.akadem.ru/projects/c2/ 2—historical Siberian silkmoth climatic (solid envelope white lines). [4], modified Triangles by indicateBaranchikov outbreaks and that Kondakov appeared (http://forest.akadem.r in 2014–2015. u/projects/c2/ (solid white lines). Triangles indicate outbreaks that appeared in 2014–2015. The Yenisey outbreak facilitated northward migration of the secondary pests that attacked The Yenisey outbreak facilitated northward migration of the secondary pests that attacked weakened by Siberian silkmoth stands. Alongside with traditional enemy of “black taiga”, Monochamus weakened by Siberian silkmoth stands. Alongside with traditional enemy of “black taiga”, urussovi, an aggressive Polygraphus proximus species was activated in fir-dominant stands in Siberia. Monochamus urussovi, an aggressive Polygraphus proximus species was activated in fir-dominant That bark beetle was responsible for massive dieback of water stress-weakened fir stands in the stands in Siberia. That bark beetle was responsible for massive dieback of water stress-weakened fir southern taiga [23]. stands in the southern taiga [23]. Along with northward migration, warming promotes opportunities for Siberian silkmoth Along with northward migration, warming promotes opportunities for Siberian silkmoth migration into mountainous areas, where low temperatures tend to limit insect population. In migration into mountainous areas, where low temperatures tend to limit insect population. In the the beginning of the 21st century, the upper silkmoth boundary was below 550 m above sea level beginning of the 21st century, the upper silkmoth boundary was below 550 m above sea level (a.s.l.) (a.s.l.) [14,22]. Meanwhile warming at higher elevations leads to prolongation of vegetation period, [14,22]. Meanwhile warming at higher elevations leads to prolongation of vegetation period, and and increase of tree growth increment and stand closure, as well as an upward advance of treeline [24]. increase of tree growth increment and stand closure, as well as an upward advance of treeline [24]. Thus, pest outbreaks above the historical boundary should be expected in the nearest future. Notably, Thus, pest outbreaks above the historical boundary should be expected in the nearest future. Notably, insect migration along an elevation gradient was recently reported in US forests [12]. insect migration along an elevation gradient was recently reported in US forests [12].

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4. Conclusions Climate change promotes migration of Siberian silkmoth outbreak range by about one degree ◦ latitude northward of the historical record. Increased warming (∆Σt>+10◦C = +120 C), vegetation period length (+9 days) and aridity increase promoted silkmoth invasion into intact northern “black ◦ taiga” forest. At present “temperature boundary” (Σt>+10◦C >1200 C) of potential outbreaks shifted northward at a distance of about 150–500 km in comparison with the boundary defined in the 1960s [4]. The Yenisei silkmoth outbreak considered here covered an area of about 800 thousand ha with conifer mortality within an area of about 300 thousand ha. Similar large-scale outbreaks hardly possible within southward taiga because pest “food base” was exhausted by former outbreaks and anthropogenic forest fragmentation. Within outbreak area silkmoth have reduced generation length from two to one year, the fact that is not trivial for the northern Siberian silkmoth population. The Yenisei outbreak is part of the panzonal (wide-spread) silkmoth outbreak in southern Siberia with distance between outbreaks locations >1000 km along latitude (Figure6). Finally, the northward migration of silkmoth will facilitate wildfire rate because pest-caused stands mortality strongly increases fire frequency and burned area (up to ten times with comparison with intact stands [25].

Acknowledgments: This research was supported by the Russian Science Fund (RNF) [grant No. 14-24-00112] and by the National Aeronautics and Space Administration (NASA)’s Terrestrial Ecology Program. Author Contributions: Viacheslav I. Kharuk: idea of research, data analysis, writing the article. Sergei T. Im: remote sensing and climate variables data analysis, writing the article; Kenneth J. Ranson: data analysis and writing the article; Mikhail N. Yagunov: remote sensing data analysis. Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; and in the decision to publish the results.

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