Species richness patterns of obligate subterranean beetles (Insecta: Coleoptera) in a global biodiversity hotspot – effect of scale and sampling intensity

We studied species richness patterns of obligate subterranean (troglobiotic) beetles in the Dinaric karst of the western Balkans, using five grid sizes with cells of 80 × 80, 40 × 40, 20 × 20, 10 × 10, and 5 × 5 km. The same two hotspots could be recognized at all scales, although details differed. Differences in sampling intensity were not sufficient to explain these patterns. Correlations between number of species and number of sampled localities increased with increasing cell size. Additional species are expected to be found in the region, as indicated by jackknife 1, jackknife 2, Chao2, bootstrap, and incidence‐based coverage (ICE) species richness estimators. All estimates increased with increasing cell size, except Chao2, with the lowest prediction at the intermediate 20 × 20 km cell size. Jackknife 2 and ICE gave highest estimates and jackknife 1 and bootstrap the lowest. Jackknife 1 and bootstrap estimates changed least with cell size, while the number of single cell species increased. In the highly endemic subterranean fauna with many rare species, bootstrap may be most appropriate to consider. Positive autocorrelation of species numbers was highest at 20 × 20 km scale, so we used this cell size for further analyses. At this scale we added 137 localities with less positional accuracy to 1572 previously considered, and increased 254 troglobiotic species considered to 276. Previously discovered hotspots and their positions did not change, except for a new species‐rich cell which appeared in the south‐eastern region. There are two centres of troglobiotic species richness in the Dinaric karst. The one in the north‐west exhibited high species richness of Trechinae (Carabidae), while in the south‐east, the Leptodirinae (Cholevidae) were much more diverse. These centres of species richness should serve as the starting point for establishing a conservation network of important subterranean areas in Dinaric karst.     

Spatial autocorrelation and red herrings in geographical ecology

Aim Spatial autocorrelation in ecological data can inflate Type I errors in statistical analyses. There has also been a recent claim that spatial autocorrelation generates ‘red herrings’, such that virtually all past analyses are flawed. We consider the origins of this phenomenon, the implications of spatial autocorrelation for macro‐scale patterns of species diversity and set out a clarification of the statistical problems generated by its presence. Location To illustrate the issues involved, we analyse the species richness of the birds of western/central Europe, north Africa and the Middle East. Methods Spatial correlograms for richness and five environmental variables were generated using Moran's I coefficients. Multiple regression, using both ordinary least‐squares (OLS) and generalized least squares (GLS) assuming a spatial structure in the residuals, were used to identify the strongest predictors of richness. Autocorrelation analyses of the residuals obtained after stepwise OLS regression were undertaken, and the ranks of variables in the full OLS and GLS models were compared. Results Bird richness is characterized by a quadratic north–south gradient. Spatial correlograms usually had positive autocorrelation up to c. 1600 km. Including the environmental variables successively in the OLS model reduced spatial autocorrelation in the residuals to non‐detectable levels, indicating that the variables explained all spatial structure in the data. In principle, if residuals are not autocorrelated then OLS is a special case of GLS. However, our comparison between OLS and GLS models including all environmental variables revealed that GLS de‐emphasized predictors with strong autocorrelation and long‐distance clinal structures, giving more importance to variables acting at smaller geographical scales. Conclusion Although spatial autocorrelation should always be investigated, it does not necessarily generate bias. Rather, it can be a useful tool to investigate mechanisms operating on richness at different spatial scales. Claims that analyses that do not take into account spatial autocorrelation are flawed are without foundation.     

Modelling geographical patterns in species richness using eigenvector-based spatial filters

Aim To test the mechanisms driving bird species richness at broad spatial scales using eigenvector‐based spatial filtering. Location South America. Methods An eigenvector‐based spatial filtering was applied to evaluate spatial patterns in South American bird species richness, taking into account spatial autocorrelation in the data. The method consists of using the geographical coordinates of a region, based on eigenanalyses of geographical distances, to establish a set of spatial filters (eigenvectors) expressing the spatial structure of the region at different spatial scales. These filters can then be used as predictors in multiple and partial regression analyses, taking into account spatial autocorrelation. Autocorrelation in filters and in the regression residuals can be used as stopping rules to define which filters will be used in the analyses. Results Environmental component alone explained 8% of variation in richness, whereas 77% of the variation could be attributed to an interaction between environment and geography expressed by the filters (which include mainly broad‐scale climatic factors). Regression coefficients of environmental component were highest for AET. These results were unbiased by short‐scale spatial autocorrelation. Also, there was a significant interaction between topographic heterogeneity and minimum temperature. Conclusion Eigenvector‐based spatial filtering is a simple and suitable statistical protocol that can be used to analyse patterns in species richness taking into account spatial autocorrelation at different spatial scales. The results for South American birds are consistent with the climatic hypothesis, in general, and energy hypothesis, in particular. Habitat heterogeneity also has a significant effect on variation in species richness in warm tropical regions.     

Species richness change across spatial scales

Humans have elevated global extinction rates and thus lowered global scale species richness. However, there is no a priori reason to expect that losses of global species richness should always, or even often, trickle down to losses of species richness at regional and local scales, even though this relationship is often assumed. Here, we show that scale can modulate our estimates of species richness change through time in the face of anthropogenic pressures, but not in a unidirectional way. Instead, the magnitude of species richness change through time can increase, decrease, reverse, or be unimodal across spatial scales. Using several case studies, we show different forms of scale‐dependent richness change through time in the face of anthropogenic pressures. For example, Central American corals show a homogenization pattern, where small scale richness is largely unchanged through time, while larger scale richness change is highly negative. Alternatively, birds in North America showed a differentiation effect, where species richness was again largely unchanged through time at small scales, but was more positive at larger scales. Finally, we collated data from a heterogeneous set of studies of different taxa measured through time from sites ranging from small plots to entire continents, and found highly variable patterns that nevertheless imply complex scale‐dependence in several taxa. In summary, understanding how biodiversity is changing in the Anthropocene requires an explicit recognition of the influence of spatial scale, and we conclude with some recommendations for how to better incorporate scale into our estimates of change.     

Disparity between range map- and survey-based analyses of species richness: patterns, processes and implications

Species richness patterns are characterized either by overlaying species range maps or by compiling geographically extensive survey data for multiple local communities. Although, these two approaches are clearly related, they need not produce identical richness patterns because species do not occur everywhere in their geographical range. Using North American breeding birds, we present the first continent‐wide comparison of survey and range map data. On average, bird species were detected on 40.5% of the surveys within their range. As a result of this range porosity, the geographical richness patterns differed markedly, with the greatest disparity in arid regions and at higher elevations. Environmental productivity was a stronger predictor of survey richness, while elevational heterogeneity was more important in determining range map richness. In addition, range map richness exhibited greater spatial autocorrelation and lower estimates of spatial turnover in species composition. Our results highlight the fact that range map richness represents species coexistence at a much coarser scale than survey data, and demonstrate that the conclusions drawn from species richness studies may depend on the data type used for analyses.     

Spatial analysis of structural and tree‐ring related parameters in a timberline forest in the Italian Alps

Abstract. We investigated the variability in spatial pattern of some structural, dendrochronological and dendroclimatological features of a mixed Larix decidua‐Pinus cembra forest at the timberline in the eastern Italian Alps at fine geographical and temporal scales. Forest structure variables such as stem diameter, tree height, age and tree‐ring related parameters (yearly growth index, mean sensitivity, first order autocorrelation and some dendroclimatic variables) have been compared at various scale levels. We observed that most of the variables show positive autocorrelated structures due to both forest dynamics and fine‐scale driving forces, probably related to microrelief. Spatial structure of yearly indexed radial growth appears sensitive to extreme climatic events. Secondary succession after past disturbances drives the forest towards a structure governed by a gap regeneration dynamics that seems to ensure the different requirements of the two main tree species present. Small spatial scale studies of forest structures, especially if integrated to dendro‐ecological data, seem an efficient tool to assess the disturbance regime and species sensitivity to environmental change.     

GIS interpolations of witness tree records (1839–1866) for northern Wisconsin at multiple scales

To construct forest landscape of pre‐European settlement periods, we developed a GIS interpolation approach to convert witness tree records of the U.S. General Land Office (GLO) survey from point to polygon data, which better described continuously distributed vegetation. The witness tree records (1839–1866) were processed for a 3‐million ha landscape in northern Wisconsin, U.S.A. at different scales. We provided implications of processing results at each scale. Compared with traditional GLO mapping that has fixed mapping scales and generalized classifications, our approach allows presettlement forest landscapes to be analysed at the individual species level and reconstructed under various classifications. We calculated vegetation indices including relative density, dominance, and importance value for each species, and quantitatively described the possible outcomes when GLO records are analysed at three different scales (resolution). The 1 × 1‐section resolution preserved spatial information but derived the most conservative estimates of species distributions measured in percentage area, which increased at coarser resolutions. Such increases under the 2 × 2‐section resolution were in the order of three to four times for the least common species, two to three times for the medium to most common species, and one to two times for the most common or highly contagious species. We mapped the distributions of hemlock and sugar maple from the pre‐European settlement period based on their witness tree locations and reconstructed presettlement forest landscapes based on species importance values derived for all species. The results provide a unique basis to further study land cover changes occurring after European settlement.     

A conceptual framework for the spatial analysis of functional trait diversity

Functional trait diversity is a popular tool in modern ecology, mainly used to infer assembly processes and ecosystem functioning. Patterns of functional trait diversity are shaped by ecological processes such as environmental filtering, species interactions and dispersal that are inherently spatial, and different processes may operate at different spatial scales. Adding a spatial dimension to the analysis of functional trait diversity may thus increase our ability to infer community assembly processes and to predict change in assembly processes following disturbance or land‐use change. Richness, evenness and divergence of functional traits are commonly used indices of functional trait diversity that are known to respond differently to large‐scale filters related to environmental heterogeneity and dispersal and fine‐scale filters related to species interactions (competition). Recent developments in spatial statistics make it possible to separately quantify large‐scale patterns (variation in local means) and fine‐scale patterns (variation around local means) by decomposing overall spatial autocorrelation quantified by Moran's coefficient into its positive and negative components using Moran eigenvector maps (MEM). We thus propose to identify the spatial signature of multiple ecological processes that are potentially acting at different spatial scales by contrasting positive and negative components of spatial autocorrelation for each of the three indices of functional trait diversity. We illustrate this approach with a case study from riparian plant communities, where we test the effects of disturbance on spatial patterns of functional trait diversity. The fine‐scale pattern of all three indices was increased in the disturbed versus control habitat, suggesting an increase in local scale competition and an overall increase in unexplained variance in the post‐disturbance versus control community. Further research using simulation modeling should focus on establishing the proposed link between community assembly rules and spatial patterns of functional trait diversity to maximize our ability to infer multiple processes from spatial community structure.     

Urbanization drives cross‐taxon declines in abundance and diversity at multiple spatial scales

The increasing urbanization process is hypothesized to drastically alter (semi‐)natural environments with a concomitant major decline in species abundance and diversity. Yet, studies on this effect of urbanization, and the spatial scale at which it acts, are at present inconclusive due to the large heterogeneity in taxonomic groups and spatial scales at which this relationship has been investigated among studies. Comprehensive studies analysing this relationship across multiple animal groups and at multiple spatial scales are rare, hampering the assessment of how biodiversity generally responds to urbanization. We studied aquatic (cladocerans), limno‐terrestrial (bdelloid rotifers) and terrestrial (butterflies, ground beetles, ground‐ and web spiders, macro‐moths, orthopterans and snails) invertebrate groups using a hierarchical spatial design, wherein three local‐scale (200 m × 200 m) urbanization levels were repeatedly sampled across three landscape‐scale (3 km × 3 km) urbanization levels. We tested for local and landscape urbanization effects on abundance and species richness of each group, whereby total richness was partitioned into the average richness of local communities and the richness due to variation among local communities. Abundances of the terrestrial active dispersers declined in response to local urbanization, with reductions up to 85% for butterflies, while passive dispersers did not show any clear trend. Species richness also declined with increasing levels of urbanization, but responses were highly heterogeneous among the different groups with respect to the richness component and the spatial scale at which urbanization impacts richness. Depending on the group, species richness declined due to biotic homogenization and/or local species loss. This resulted in an overall decrease in total richness across groups in urban areas. These results provide strong support to the general negative impact of urbanization on abundance and species richness within habitat patches and highlight the importance of considering multiple spatial scales and taxa to assess the impacts of urbanization on biodiversity.     

Quantifying soil organic carbon in complex landscapes: an example of grassland undergoing encroachment of woody plants

The invasion of woody plants into grass‐dominated ecosystems has occurred worldwide during the past century with potentially significant impacts on soil organic carbon (SOC) storage, ecosystem carbon sequestration and global climate warming. To date, most studies of tree and shrub encroachment impacts on SOC have been conducted at small scales and results are equivocal. To quantify the effects of woody plant proliferation on SOC at broad spatial scales and to potentially resolve inconsistencies reported from studies conducted at fine spatial scales, information regarding spatial variability and uncertainty of SOC is essential. We used sequential indicator simulation (SIS) to quantify spatial uncertainty of SOC in a grassland undergoing shrub encroachment in the Southern Great Plains, USA. Results showed that both SOC pool size and its spatial uncertainty increased with the development of woody communities in grasslands. Higher uncertainty of SOC in new shrub‐dominated communities may be the result of their relatively recent development, their more complex above‐ and belowground architecture, stronger within‐community gradients, and a greater degree of faunal disturbance. Simulations of alternative sampling designs demonstrated the effects of spatial uncertainty on the accuracy of SOC estimates and enabled us to evaluate the efficiency of sampling strategies aimed at quantifying landscape‐scale SOC pools. An approach combining stratified random sampling with unequal point densities and transect sampling of landscape elements exhibiting strong internal gradients yielded the best estimates. Complete random sampling was less effective and required much higher sampling densities. Results provide novel insights into spatial uncertainty of SOC and its effects on estimates of carbon sequestration in terrestrial ecosystem and suggest effective protocol for the estimating of soil attributes in landscapes with complex vegetation patterns.     

Scale dependency of metapopulation models used to predict climate change impacts on small mammals

To investigate potential range shifts in a changing climate it is becoming increasingly common to develop models that account for demographic processes. Metapopulation models incorporate the spatial configuration of occupied habitat (i.e. arrangement, size and quality), population demographics, and inter‐patch dispersal making them suitable for investigating potential threats to small mammal range and abundance. However, the spatial scale (resolution) used to represent species–environment dynamics may affect estimates of range shift and population resilience by failing to realistically represent the spatial configuration of suitable habitat, including stepping stones and refugia. We aimed to determine whether relatively fine‐scale environmental information influenced predictions of metapopulation persistence and range shift. Species distribution models were constructed for four small terrestrial mammals from southern Australia using environmental predictors measured at 0.1 × 0.1 km (0.01 km²) or 1.0 × 1.0 km (1 km²) resolution, and combined with demographic information to parameterise coupled niche‐population models. These models were used to simulate population dynamics projected over 40‐yr under a stable and changing climate. Initial estimates of the area of available habitat were similar at both spatial scales. However, at the fine‐scale, habitat configuration comprised a greater number of patches (ca 12 times), that were more irregular in shape (ca 8 times the perimeter:area), and separated by a tenth of the distance than at the coarse‐scale. While small patches were not more prone to extinction, populations generally declined at a higher rate and were associated with a lower expected minimum abundance. Despite increased species vulnerability at the fine‐scale, greater range shifts were measured at the coarse‐scale (for species illustrating a shift at both scales). These results highlight the potential for range shifts and species vulnerability information to be misrepresented if advanced modelling techniques incorporating species demographics and dispersal inadequately represent the scale at which these processes occur.     

Small‐scale spatial dynamics of vegetation in a grazed Uruguayan grassland

We explored the small‐scale plant species mobility in a subhumid native grassland subjected to grazing by cattle in south‐western Uruguay. We established four permanent plots of 40 × 40 cm, divided in 16 × 16 cells. In each cell, the presence of species was seasonally recorded for 2 years and annually recorded for 4 years. By nesting the cells, we studied the mobility at different scales, from 6.25 cm² to 400 cm². At each scale we measured species richness, cumulative richness and the turnover rates of the dominant species. We found that the cumulative species richness was an increasing power function, with higher accumulation rates with smaller spatial scale. Although species richness showed seasonal fluctuations, the mean species richness was constant during the study period. We detected significant spatio‐temporal variability in mobility patterns among species. Certain species showed a high capacity to colonize new sites, whereas other species rotate among sites that they previously occupied. Grazed communities in Uruguayan Campos are structured as a dense matrix of perennials grasses and forbs, where vegetative propagation is the main form of growth of the species. The small‐scale dynamics and the high variability in the mobility characteristics could be linked with the diversity of growth forms and spatial strategies of the species in this community. We believe that a high degree of small‐scale spatial dynamics contribute to explain the species coexistence and the apparent stability of communities at local scales.     

Red herrings revisited: spatial autocorrelation and parameter estimation in geographical ecology

There have been numerous claims in the ecological literature that spatial autocorrelation in the residuals of ordinary least squares (OLS) regression models results in shifts in the partial coefficients, which bias the interpretation of factors influencing geographical patterns. We evaluate the validity of these claims using gridded species richness data for the birds of North America, South America, Europe, Africa, the ex‐USSR, and Australia. We used richness in 110×110 km cells and environmental predictor variables to generate OLS and simultaneous autoregressive (SAR) multiple regression models for each region. Spatial correlograms of the residuals from each OLS model were then used to identify the minimum distance between cells necessary to avoid short‐distance residual spatial autocorrelation in each data set. This distance was used to subsample cells to generate spatially independent data. The partial OLS coefficients estimated with the full dataset were then compared to the distributions of coefficients created with the subsamples. We found that OLS coefficients generated from data containing residual spatial autocorrelation were statistically indistinguishable from coefficients generated from the same data sets in which short‐distance spatial autocorrelation was not present in all 22 coefficients tested. Consistent with the statistical literature on this subject, we conclude that coefficients estimated from OLS regression are not seriously affected by the presence of spatial autocorrelation in gridded geographical data. Further, shifts in coefficients that occurred when using SAR tended to be correlated with levels of uncertainty in the OLS coefficients. Thus, shifts in the relative importance of the predictors between OLS and SAR models are expected when small‐scale patterns for these predictors create weaker and more unstable broad‐scale coefficients. Our results indicate both that OLS regression is unbiased and that differences between spatial and nonspatial regression models should be interpreted with an explicit awareness of spatial scale.     

Comparison of native and exotic distribution and richness models across scales reveals essential conservation lessons

Comparing native and exotic plant species distribution and richness models can help to reveal the causes of invasive exotic species proliferation and provide recommendations for preserving native‐dominated ecosystems. However, models may have limited applicability if potentially divergent patterns across scales, spatial autocorrelation and correspondence with community‐wide patterns such as species richness are not considered. I modeled the distributions of 20 dominant native and 20 dominant exotic species among and within patches in a heavily‐invaded and threatened ecosystem in western North America, examining the roles of scale and species origin on variable selection, spatial autocorrelation and model accuracy to determine conditions that favour native over exotic dominants, and derive recommendations for effective management. I also compared distribution models with native and exotic species richness models, to determine the extent to which dominant native and exotic species were representative of synoptic community patterns. Predictability was lower for exotic dominants, possibly because they are environmental generalists, and was lower within than among patches. Predictors were generally shared between distribution and richness models; however, species‐specific differences were common within both native and exotic species groups. Predictors for individual species across scales were frequently different and sometimes opposing. Distribution and richness models suggest that management assuming environmental affiliation at one scale may be ineffective at another; that site prioritization to maximize native versus exotic richness may not preserve the habitat of some common native species; and that intensive management to reduce exotics may be difficult due to low predictability and shared affiliations with natives. Comparing native and exotic distribution and richness models at two scales enabled scale‐specific conservation recommendations and elucidated trade‐offs between management for richness and representation that distribution models at an individual scale would not have allowed.     

Decomposing the heterogeneity of species distributions into multiple scales: a hierarchical framework for large‐scale count surveys

We introduce a novel spatially explicit framework for decomposing species distributions into multiple scales from count data. These kinds of data are usually positively skewed, have non‐normal distributions and are spatially autocorrelated. To analyse such data, we propose a hierarchical model that takes into account the observation process and explicitly deals with spatial autocorrelation. The latent variable is the product of a positive trend representing the non‐constant mean of the species distribution and of a stationary positive spatial field representing the variance of the spatial density of the species distribution. Then, the different scales of emergent structures of the distribution of the population in space are modelled from the latent density of the species distribution using multi‐scale variogram models. Multi‐scale kriging is used to map the spatial patterns previously identified by the multi‐scale models. We show how our framework yields robust and precise estimates of the relevant scales both for spatial count data simulated from well‐defined models, and in a real case‐study based on seabird count data (the common guillemot Uria aalge) provided by large‐scale aerial surveys of the Bay of Biscay (France) performed over a winter. Our stochastic simulation study provides guidelines on the expected uncertainties of the scales estimates. Our results indicate that the spatial structure of the common guillemot can be modelled as a three‐level hierarchical system composed of a very broad‐scale pattern (～ 200 km) with a stable location over time that might be environmentally controlled, a broad‐scale pattern (～ 50 km) with a variable shape and location, that might be related to shifts in prey distribution, and a fine‐scale pattern (～ 10 km) with a rather stable shape and location, that might be controlled by behavioural processes. Our framework enables the development of robust, scale‐dependent hypotheses regarding the potential ecological processes that control species distributions.     

Spatial scale affects bioclimate model projections of climate change impacts on mountain plants

Plant species have responded to recent increases in global temperatures by shifting their geographical ranges poleward and to higher altitudes. Bioclimate models project future range contractions of montane species as suitable climate space shifts uphill. The species–climate relationships underlying such models are calibrated using data at either ‘macro’ scales (coarse resolution, e.g. 50 km × 50 km, and large spatial extent) or ‘local’ scales (fine resolution, e.g. 50 m × 50 m, and small spatial extent), but the two approaches have not been compared. This study projected macro (European) and local models for vascular plants at a mountain range in Scotland, UK, under low (+1.7 °C) and high (+3.3 °C) climate change scenarios for the 2080s. Depending on scenario, the local models projected that seven or eight out of 10 focal montane species would lose all suitable climate space at the site. However, the European models projected such a loss for only one species. The cause of this divergence was investigated by cross‐scale comparisons of estimated temperatures at montane species' warm range edges. The results indicate that European models overestimated species' thermal tolerances because the input coarse resolution climate data were biased against the cold, high‐altitude habitats of montane plants. Although tests at other mountain ranges are required, these results indicate that recent large‐scale modelling studies may have overestimated montane species' ability to cope with increasing temperatures, thereby underestimating the potential impacts of climate change. Furthermore, the results suggest that montane species persistence in microclimatic refugia might not be as widespread as previously speculated.     

Fine‐scale heterogeneity in beetle assemblages under co‐occurring Eucalyptus in the same subgenus

Aim Insect biodiversity is often positively associated with habitat heterogeneity. However, this relationship depends on spatial scale, with most studies focused on differences between habitats at large scales with a variety of forest tree species. We examined fine‐scale heterogeneity in ground‐dwelling beetle assemblages under co‐occurring trees in the same subgenus: Eucalyptus melliodora A. Cunn. ex Schauer and E. blakelyi Maiden (Myrtaceae). Location Critically endangered grassy woodland near Canberra, south‐eastern Australia. Methods We used pitfall traps and Tullgren funnels to sample ground‐dwelling beetles from the litter environment under 47 trees, and examined differences in diversity and composition at spatial scales ranging from 100 to 1000 m. Results Beetle assemblages under the two tree species had distinctive differences in diversity and composition. We found that E. melliodora supported a higher richness and abundance of beetles, but had higher compositional similarity among samples. In contrast, E. blakelyi had a lower abundance and species richness of beetles, but more variability in species composition among samples. Main conclusions Our study shows that heterogeneity in litter habitat under co‐occurring and closely related eucalypt species can influence beetle assemblages at spatial scales of just hundreds of metres. The differential contribution to fine‐scale alpha and beta diversity by each eucalypt can be exploited for conservation purposes by ensuring an appropriate mix of the two species in the temperate woodlands where they co‐occur. This would help not only to maximize biodiversity at landscape scales, but also to maintain heterogeneity in species richness, trophic function and biomass at fine spatial scales.     

Structural heterogeneity and spatial autocorrelation in a natural mature Pinus sylvestris dominated forest

The tree species composition, vertical stratification and patterns of spatial autocorrelation at the tree and quadrate (25 × 25 m) scales were studied in a natural mature PinuS sylvestris dominated forest in eastern Finland. For the analyses we mapped the locations and dimensions of trees taller than 10 m in a 9 ha (300 × 300 m) area, and within this area we mapped all trees taller than 0.3 m on a core plot of 4 ha (200 × 200 m). The overall tree size distribution was bimodal. the dominant layer and the understory forming the peak frequencies. Pinus sylvestris dominated the main canopy, together with scattered Betula pendula and Picea abies. Alnus incana, Populus tremula, Salix caprea, Sorbus aucuparia and Juniperus communis occurred only in the under- and middlestories. Autocorrelation analysis (semivarianee) of tree size variation revealed spatial patterns, which were strongly dependent on the size of trees included in the analysis. When all living trees, including the understory regeneration, were taken into account, the autocorrelation pattern ranged up to 35 m inter-tree distances, reflecting the spatial scale of understory regeneration patches. Competitive interaction among middle- and upperstory trees (height>10 m) had contrasting effects on autocorrelation pattern depending on spatial scale. At the fine scale, dominant trees suppressed their smaller close neighbors (asymmetric competition), which was shown as increased tree size variation at small inter-tree distances (<2 m). At slightly larger inter-tree distances, specifically among large trees of similar size, competition was more symmetrical, which resulted in decreased tree size variation at these inter-tree distances (3–4 m). This effect was seen most clearly in the dominant trees, there being a clear autocorrelation pattern in tree size up to inter-tree distances of ～4 m. At the quadrate scale (25 × 25 m) the analysis revealed high local variation in structural characteristics such as tree height diversity (THD), tree species diversity (H) and autocorrelation of tree height. The analysis suggests that naturally developed P. sylvestris forests exhibit complex small-scale patterns of structural heterogeneity and spatial autocorrelation in tree size. These patterns may be important for stand-scale habitat diversity and can have aggregated effects on ecosystem dynamics at larger spatial scales though their influence on the spread of disturbance and regeneration after disturbance.