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Context: Forest microclimates differ from regional macroclimates because forest canopies affect energy fluxes near the ground. However, little is known about the environmental drivers of understorey temperature heterogeneity and its effects on species assemblages, especially at landscape scales. Objectives: We aimed to identify which temperature variables best explain the landscape-scale distribution of forest vegetation and to disentangle the effects of elevation, terrain attributes and canopy cover on understorey temperatures. Methods: We measured growing season air temperature, canopy cover and plant community composition within 46 plots established across a 400-km2 area in Czech Republic. We linked growing season maximum, mean and minimum temperatures with elevation, canopy cover and topographic proxies for heat load, topographic position, soil moisture and cold air drainage, and created fine-scale topoclimatic maps of the region. We compared the biological relevance of in situ measured temperatures and temperatures derived from fine-scaled topoclimatic maps and global WorldClim 2 maps. Results: Maximum temperature was the best predictor of understorey plant species composition. Landscape-scale variation in maximum temperature was jointly driven by elevation and terrain topography ( R adj. 2 = 0.79) but not by canopy cover. Modelled maximum temperature derived from our topoclimatic maps explained significantly more variation in plant community composition than WorldClim 2 grids. Conclusions: Terrain topography creates landscape-scale variation in maximum temperature, which in turn controls plant species assembly within the forest understorey. Maximum temperature is therefore an important but neglected microclimatic driver of species distribution across landscapes. [ABSTRACT FROM AUTHOR]

Background: Severe canopy-removing disturbances are native to many temperate forests and radically alter stand structure, but biotic legacies (surviving elements or patterns) can lend continuity to ecosystem function after such events. Poorly understood is the degree to which the structural complexity of an old-growth forest carries over to the next stand. We asked how pre-disturbance spatial pattern acts as a legacy to influence post-disturbance stand structure, and how this legacy influences the structural diversity within the early-seral stand. Methods: Two stem-mapped one-hectare forest plots in the Czech Republic experienced a severe bark beetle outbreak, thus providing before-and-after data on spatial patterns in live and dead trees, crown projections, down logs, and herb cover. Results: Post-disturbance stands were dominated by an advanced regeneration layer present before the disturbance. Both major species, Norway spruce (Picea abies) and rowan (Sorbus aucuparia), were strongly self-aggregated and also clustered to former canopy trees, pre-disturbance snags, stumps and logs, suggesting positive overstory to understory neighbourhood effects. Thus, although the disturbance dramatically reduced the stand’s height profile with ~100% mortality of the canopy layer, the spatial structure of post-disturbance stands still closely reflected the pre-disturbance structure. The former upper tree layer influenced advanced regeneration through microsite and light limitation. Under formerly dense canopies, regeneration density was high but relatively homogeneous in height; while in former small gaps with greater herb cover, regeneration density was lower but with greater heterogeneity in heights. Conclusion: These findings suggest that pre-disturbance spatial patterns of forests can persist through severe canopy-removing disturbance, and determine the spatial structure of the succeeding stand. Such patterns constitute a subtle but key legacy effect, promoting structural complexity in early-seral forests as well as variable successional pathways and rates. This influence suggests a continuity in spatial ecosystem structure that may well persist through multiple forest generations. [ABSTRACT FROM AUTHOR]

We used the newly formed database of phytoplankton samples from the Czech Republic, containing 696 taxa from 662 samples of various types of stagnant waters (fishponds, alluvial backwaters, flooded sand- and gravel pits, lakes in abandoned quarries in former coal mines, reservoirs and others) to test for the relationships between phytoplankton composition and productivity, geographical and climatic variables (elevation, temperature, precipitation) and season of the year. As a surrogate of productivity, the number of cells/ml and the total biovolume[μm 3 ]/ml, both spanning over more than six orders of magnitude (from 10 1 to 10 7 for the number of cells and from 10 2 to 10 9 for the biovolume) were used. The phytoplankton was characterized by its species composition and also by the composition of large taxonomic groups. The data were analysed by constrained ordination (Canonical Correspondence Analysis), including variation partitioning, and by ANOVA of the estimates of species optima based on weighted averages of environmental characteristics. All the explanatory variable groups, i.e. productivity, geography/climate, and season have significant effects on both the species composition and the composition of large taxonomic groups. Productivity is the best predictor of both species and large taxonomic group composition, followed by climatic variables and finally season. The relative effectiveness of productivity as a predictor was considerably greater for large taxonomic groups. The productivity characterized by the number of cells was always a better predictor than when characterized by biovolume. The species optima estimated as weighted averages of corresponding environmental variables show consistent patterns according to large taxonomic groups, but also according to the genera within the groups: in particular, the cyanobacteria and Chrysophyceae preferred on average the most and the least productive environments respectively, however, there were large differences in species preferences also within groups and even within genera. The optima of species on the trophic gradient are suitable characteristics for ecological indication and are presented for more than 400 taxa in the appendix together with estimates of species tolerance. [ABSTRACT FROM AUTHOR]

Understanding the effect of climate on the distribution and composition of vegetation is crucial to investigate its response to climate change. However, process-based vegetation models usually use comparably coarse-grain climatic data, which underestimate topographic effects on local climate.To evaluate potential effects of topoclimate on vegetation redistribution predicted by the process-based vegetation model LPJmL, we compared modelled vegetation based on different climate inputs, namely coarse-grained climatic data, data corrected for temperature lapse rate according to local elevation and data incorporating topographic effects responsible for cool-air pooling and anisotropic heat load in a hilly region in the Czech Republic.The results showed the pronounced effect of terrain topography as well the large discrepancy between coarse-scale climate and locally adapted climate. Our results thus give a hint on how sensitive modelled vegetation distribution is to climate input correction. We found that despite future climate change, including higher temperatures and less precipitation, the current vegetation might be able to remain in some local refuges, which are however not captured by coarse-scale climatic dataset. Therefore, the inclusion of topographic effects is crucial for realistic estimates of the vegetation redistribution in the response to climate change. [ABSTRACT FROM AUTHOR]

Biodiversity monitoring is crucial for ecosystem conservation, but ground data collection is limited by cost, time, and scale. Remote sensing is a convenient approach providing frequent, near-real-time information with fine resolution over wide areas. According to the Spectral Variation Hypothesis (SVH), spectral diversity (SD) is an effective proxy of environmental heterogeneity, which ultimately relates to plant diversity. So far, studies testing the relationship between SD and biodiversity have reported contradictory findings, calling for a thorough investigation of the key factors (i.e., metrics applied, habitat type, scale, and temporal effects) and conditions under which such a relationship exists. This study investigates the applicability of the SVH for monitoring plant diversity at the landscape scale by comparing the performance of three types of SD metrics. Species richness and functional diversity were calculated for >2000 grid cells of 5′ × 3′ covering the Czech Republic. Within each cell, we quantified SD using a Landsat-8 "greenest pixel" composite by applying (i) the standard deviation of NDVI, (ii) Rao's Q entropy index and (iii) the richness of "spectral communities". Habitat type (i.e., land cover) was included in the models of the relationship between SD and ground biodiversity. Both species richness and functional diversity showed positive and significant relationships with each SD metric tested. However, SD alone accounted for a small fraction of the deviance explained by the models. Furthermore, the strength of the relationship depended significantly on habitat type and was highest in natural areas with transitional bushy and herbaceous vegetation. Our results underline that despite the stability of the significance of the relationship between SD and plant diversity at this scale, the applicability of SD for biodiversity monitoring is context-dependent and the factors mediating such a relationship must be carefully considered to avoid misleading conclusions. • At the landscape scale, plant diversity is positively related to spectral diversity. • Spectral diversity alone explains a small fraction of total biodiversity variability. • Slight differences among the performances of the spectral diversity metrics tested. • The relationship between spectral and plant diversity is context-dependent. [ABSTRACT FROM AUTHOR]