Tree diversity patterns along the latitudinal gradient in the northwestern Russia

More than 20% of forests on the Earth are located in the Russian Federation with the total area of forests exceeding 800 million hectares (FAO 2015). Among them boreal forests are prevailing with proportion 86%. A credible estimate of the current forest condition and ongoing dynamics is a basis for reliable forecasts of the global changes and sustainable forest management. Nowadays the most worldwide common approach for assessment of forest conditions and dynamics is based on the systematic ground-based forest monitoring, performed at a set of regularly structured locations (Ferretti 2013; Crowther et al. 2015). In Europe, one of the most ambitious projects on forest description on a regular basis is the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests).

One of the key forest characteristics is the biodiversity, particularly the diversity of woody plants which are forest ecosystem engineers (Jones et al. 1994; Jones et al. 1997). There is evidence of a positive relationship between the tree species diversity and ecosystem functioning (Gamfeldt et al. 2013). Recent studies have shown that ICP Forests data can be successfully used to assess tree diversity (Tröltzsch et al. 2009; Brus et al. 2012). In Russia the monitoring network ICP Forests spans over 1700 km along the western Russian border and covers six northwestern regions (Lukina et al. 2013). Just as everywhere else on our planet, Russian forests are generally shaped by a combination of various factors, which can be divided into four principal groups: historic, current abiotic, biotic and anthropogenic (Huston 1994). It is recognized that the modern differentiation of the tree species composition on a latitudinal gradient is determined primarily by climate (Sykes et al. 1996; Körner et al. 2016; Vetluzhskikh 2016), and for boreal and northern temperate European forests the most important factor is temperature (Morozova 2009). However, other factors acting on different spatial scales may have a significant impact on the local patterns of tree species richness (Caley and Schluter 1997; Svenning and Skov 2005; Svenning et al. 2010). While regional factors (climate and historical) determine the regional pool of species, at a local level other factors such as soil, biotic interactions, natural disturbances and anthropogenic influence may be important. As Caley and Schluter (1997) noted it is also possible “that in natural communities a variety of such processes could interact, resulting in a complex array of outcomes”. As several key fundamental studies in Russia indicated the crucial contribution of wildfires into structure and composition of forest communities (Molchanov 1934; Korchagin 1954), we specifically test the validity of this concept nowadays and pay increased attention to pyrogenic factor.

This study is aimed at assessment of tree species diversity patterns depending on combination of various natural and anthropogenic factors in Northwestern Russia.

To answer these scientific questions we performed series of statistical analyses of the tree species distribution and diversity along the latitudinal gradient in Northwestern Russia based on the tree data collected within the ICP Forests monitoring programme. It is the first study on tree species diversity in the large area in European Russia, based on data from the regular grid.

According to the Maxent species distribution modelling results only 3 of the observed species (Picea abies, Pinus sylvestris, Betula sp.) are not theoretically restricted in their distribution due to climatic conditions over the study area (Fig. 2). The most influential factors for Pinus sylvestris distribution revealed to be the temperature characteristics: mean Diurnal range (BIO2), temperature seasonality (BIO4) and max temperature of warmest month (BIO5). Betula sp. and Picea abies also were found to be strongly dependent on these factors, but also considerably influenced by the precipitations seasonality (BIO15). The optimal distribution ranges for Alnus incana and Alnus glutinosa completely overlap with the northern border at 61°N, and these species revealed to be most sensitive to the altitude and the precipitation of driest quarter (BIO17). Distribution of Populus tremula is concentrated in the southern regions of the study area. Our modelling results depict that it is most influenced due to BIO4, BIO5 and BIO17. It is also worth to mention that according to Maxent results potential distribution range of Populus tremula spans so far north as the Kola Peninsula, which considerably deviates from its observed distribution range.

The maximum tree diversity of studied forest communities had a distinguished latitudinal structure varying from typical 1–3 species encountered in the north taiga sites to 5 (occasionally 7) species encountered in south taiga and hemiboreal sites (Fig. 3a). However, the mono-species communities were rather abundant in all bioclimatic zones, with the highest proportion of mono-species communities observed in south taiga. Among the mono-species communities 72% were pine forests and 12% birch forests. Especially prevailing part of mono-species pine forests was detected in sandur parts of south taiga and lake-glacial sandy landscapes in hemiboreal zone. Rich species communities mainly consisted of spruce and broadleaf species, although spruce was not particularly associated with rich communities. These patterns revealed by descriptive analysis were further supported by the results of joint species distribution modelling, which indicated positive associations within several groups of broadleaf species and negative associations of most species with pine (Fig. 4). The HMSC estimated that there are three statistically significant latent factors, two of which were found to be spatially structured with non-zero spatial ranges (40–70 km). The averaged over species Tjur R ² was 0.34, although it significantly varied for individual species (Table 3). The third latent factor was primarily associated with increased occurrence probability of Pinus sylvestris and decreased probability to encounter any other species in the same plot. The remaining two latent factors could not be easily attributed to single species, the first latent factor seems to be associated with divisions of tree species into nemoral and boreal groups.

Species	LatLoad1	LatLoad2	LatLoad3	Tjur R 2
Acer platanoides	−0.018	0.003	0	0.13
Acer pseudoplatanus	−0.011	−0.026	0	0.61
Alnus glutinosa	−0.034	−0.053	0.085	0.26
Alnus incana	0.027	−0.001	0.099	0.2
Betula sp.	0.02	−0.016	0.073	0.07
Carpinus betulus	−0.067	0.03	0	0.65
Fraxinus excelsior	−0.045	−0.041	0.002	0.55
Larix sp.	−0.011	−0.026	0	0.61
Picea abies	0.022	−0.011	0.003	0.01
Pinus sylvestris	0.067	0.003	−0.326	0.68
Populus tremula	0.031	0.017	0.167	0.22
Quercus robur	−0.109	−0.004	0.001	0.71
Salix sp.	0.003	0.001	0.002	0.01
Sorbus aucuparia	0.002	0	0.003	0.01
Tilia cordata	−0.075	0.034	0	0.69
Ulmus sp.	−0.002	−0.001	0.001	0.01

The spatial distribution dominant tree ages was highly uneven (Fig. 3b). The northern areas were generally represented by highly mature forests (>160 years) with the most aged trees (300–330 years) being pines or spruces observed in the north taiga. Further to the south the proportion of the forest stands of age 80–120 years increases. However, while in the Karelian Isthmus this age group is vastly prevailing, Karelian middle taiga features more heterogeneous age distribution mainly represented by middle age stands (40–80 years) with occasional more mature stands (120–160 years). More southern regions of Pskov, Novgorod and Kaliningrad generally covered by middle age forests while the mature stands (>120 years) are rare.

First of all it is worth noting that ICP Forests data have some limitations when used for analysis of tree species diversity (Tröltzsch et al. 2009). The plot density 16 km × 16 km to 32 km × 32 km provides only for coarse estimation of current species distribution at the regional level. Furthermore the ICP Forests plot network includes not all trees with stems exceeding a certain diameter but those related to 1–3 Kraft’s classes. Also plot areas may be not sufficient for adequately measure tree species diversity (Latham and Ricklefs 1993). However in general ICP Forests plot data are considered to be representative at the regional level and there is positive experience of their use for biodiversity assessment (Tröltzsch et al. 2009; Meier et al. 2012). Finally, as it was already mentioned, for a large area in Northwest Russia these data are the only available for analysis with regular survey grid.

Extremely low tree diversity is characteristic of all Eurasian boreal forest (Wirth 2005). But woody flora of temperate European forests is significantly less diverse than flora of forests in the North America and especially in East Asia at the similar latitude range. That is usually explained by massive extinction in the Pleistocene ice ages (Huntley 1993; Latham and Ricklefs 1993; Svenning et al. 2008).

Our data clearly demonstrated the latitudinal gradient of tree species diversity – with 6 species encountered in the northern and middle taiga, 8 species in the southern taiga, and 15 in hemiboreal subzone. The highest diversity is observed in the Kaliningrad region – the most south and that is also important, the most western region in this study. It is the only region where temperate Central European tree species have been presented, e.g. Carpinus betulus and Acer pseudoplatanus. The northern limits of species distribution are generally associated with duration of growing season, while the eastern limits – with temperatures of the coldest month (Woodward and Williams 1987; Sykes et al. 1996).

Climate regulates tree species composition at the regional level; however, at the local level site-specific factors (e.g. forest fires, wind throw, topography, soil characteristics, interspecific competition and successional stage) mostly overrule the importance of regional climate (Pearson and Dawson 2003; Körner et al. 2016; Kuosmanen et al. 2016).

For example widespread distribution of monocultural pine forests in the study area could be associated with poor soils, anthropogenic influence and early stage of successions. As it was demonstrated for the area of Les Landes, SW France (Sykes et al. 1996), most of the moisture-demanding tree species were absent from the sandy soils. Slash-and-burn cultivation practices with shorter fire return intervals and agricultural deforestation also caused change towards monocultural forest structure (Kuosmanen et al. 2016).

Our results of modelling the potential distribution of tree species could be explained by ecological characteristics of these species. For example, the distribution of mesofilic species (Betula sp. and Picea abies) was limited to the areas with moist soil during the growing season – in contrast to xerofilous species Pinus sylvestris, which can successfully tolerate long-term moisture deficit. The altitudinal dependence of Alnus incana and Alnus glutinosa distribution could be attributed to these species’ preference to moist floodplains. Higher altitude results in faster soil drainage, leading to suboptimal habitats and consequently lower occurrence probabilities. The relation between tree species distribution and habitat type fall in line with both MaxEnt modelling results and previous findings on pine forest being associated to poor oligotrophic soils and spruce preferences to over-moist soils (Yurkovskaya 2014).

Notably however, our results exhibited huge deviation between actual and estimated potential distribution area of aspen, with the MaxEnt model suggesting that it could be encountered so far north as the Kola Peninsula. Aspen is a mesophilic and mesotrophic species, prefers moist, rich soil and rarely could be encountered on dry sandy or swampy soils (Mikhailov 1985). In Northwestern Russia sandy soil and swampy areas that are unsuitable to well sustain growth of aspen, are common. Thus, the low occurrence rates of aspen on its northern border could be attributed to suboptimal habitats. Aspen occurrence on the Kola Peninsula could be related to habitats with rich and moist soils, for example, in the Khibiny Mountains (Mishkin 1953).

Results obtained with HMSC simulation revealed fundamental differences between temperate and boreal tree species. Not going into further details we note that historically these groups of species exhibited different behavior. Recent research show that during glacial period boreal tree species were wide spread in Central and Eastern Europe, including the Russian Plain, while temperate species were preserved in small refugia restricted to the Mediterranean and Black Sea regions (Svenning et al. 2008).

The spatial distribution of dominant tree ages was decently maps to forest use intensity over the study area. As the clearcutting in Karelia started only around 1930 (Gromtsev 2003, 2015; Volkov 2008) due to lack of infrastructure and low population density, these territories still keep very mature forest stands, which age significantly exceeds the assigned cutting age. The proportion of mostly intact natural forest landscapes in North Russia is estimated by 13% (Yaroshenko et al. 2001). On the contrary, forestry has been much more intensive in the southern regions for already several hundred years and that lead to lowered tree stand ages and substantial transition to secondary forests (Angelstam and Kuuluvainen 2004; Gromtsev 2015; Rautiainen et al. 2016). For Fennoscandia the similar consistent pattern was observed (Kuosmanen et al. 2016), although forests in Russia still remain their natural features.

Over many ages, the pyrogenic factor was governing the forest forming processes in the boreal zone (Drobyshev et al. 2015; Kuosmanen et al. 2016). Sustenance of pine stands in north and middle taiga is related to periodic stand replacing wildfires, which occur several times per millennium (Molchanov 1934; Korchagin 1954; Gromtsev 2002). While the crown fires create preferable habitats for pine and birch colonization, spruce is completely intolerant even to ground fires (Korchagin 1954). Thus, the fire caused death of mature trees, as well as spruce and birch offspring and the remained pine restored dominantly over the burnt area. But nowadays, the wildfire density is higher in the southern regions, where secondary deciduous and coniferous-broadleaf forests are encountered more often (Fig. 3c). Moreover, the lower density of fires in the north taiga, compared to the middle taiga, is due to shorter fire danger season and increased proportion of swampy areas (Drobyshev et al. 2014).

Nowadays, contribution of wildfire into forest shaping is gradually decreasing in the study area. For example, in Karelia the mean single burnt area decreased several times over few last decades despite of approximately constant amount of ignitions (Gromtsev 2008). Our analysis of satellite data over 2006–2016 confirms that the amount of fire events and burnt area are decreasing over the last decade. Several authors relate this phenomenon to climate change: recently the amount of heavy rainfalls (>10 mm) in this area has significantly increased (Filatov et al. 2012). In 1995–2010 the number of heavy rainy days was considerably higher than could be expected from long-term observations. As the heavy rainfalls often stop the fire spreading, we can hypothesize that increased precipitation amount could be the reason for decrease in significance of fire in forest forming processes.

In many parts of the study area the major changes in forest composition occurred during last several decades. In Karelia the area of pine forest was decreased by 36%, the area of deciduous forests was increased more than 2.5 times, and the area of spruce forests remained unchanged (Gromtsev 2003). At the same time in the Karelian Isthmus the area of pine forests decreased by 13% in the period of 1923–1983, while the area of spruce forest increased by similar proportion (Fedorchuk et al. 2005). The optimal distribution area for pine, spruce and birch completely overlap, and with the continuing trend of reducing the frequency and intensity of fires, it is reasonably hypothesized that spruce will further replace pine and birch in these regions.

Local biodiversity of tree species in Northwestern Russia is determined by a set of environmental and anthropogenic factors. The north taiga is distinguished for its lowest biodiversity with no more than 3 tree species being observed at same location. On the opposite edge of the latitudinal range lie the areas with highest biodiversity, caused by high proportion of broadleaf species in community compositions. Monocultural stands could be found through the whole study area, being typically represented by pine and corresponding mainly to sandur and lake-glacial landscapes as well as sandy soils.

We have demonstrated that ICP Forests monitoring network enables to successfully establish the main qualitative and quantitative relationships of the spatial variation of tree diversity with respect to climatic, landscape, soil and anthropogenic disturbance factors. However, obtaining robust inference of the ongoing dynamics is problematic given that we have observed only a “snapshot” of the dynamic ecosystem. We believe that in order to resolve this issue, the considered data has to be of spatial time-series nature, which requires regular recensus of sites in the monitoring network.