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Aim Locally abundant species are typically widespread, while locally scarce species are geographically restricted—the so‐called abundance‐occupancy relationships (AORs). AORs help explain the drivers of species rarity and community assembly, but little is known about how variation around such relationship is driven by species traits and niche‐based processes, particularly in tropical woody plants. We tested the hypothesis that AORs in tropical dryland woody plants are positive and mediated by niche and functional traits along environmental gradients. Location The Caatinga dry forest and Cerrado savannah, Brazil. Methods We aggregated abundance and occurrence data into grid‐cells representing local (10‐km) to landscape scales (50‐km). We calculated species mean relative abundance at occupied grid‐cells (local abundance) and the proportion of grid‐cells occupied (occupancy), and estimated their niche breadth and marginality along multivariate environmental gradients. Results AORs were positive but weak at different scales in both regions due to some locally abundant but geographically restricted species, with most species being both locally and geographically rare. Cross‐species variation in local abundance was largely unpredictable, but occupancy was strongly driven by niche and functional traits, with a prominent negative effect of niche marginality. Geographically restricted species were associated with rare habitats, such as wetter and less intensively used habitats. Large seeds and abiotic dispersal favoured occupancy in Caatinga at small and large spatial scales, respectively, whereas species with conservative leaves were more widespread across scales in Cerrado. Main conclusions Woody plants in dry tropical biotas exhibit weak AORs, a pattern likely related to low habitat availability and dispersal limitation. Caatinga and Cerrado biotas emerge as environmentally structured at multiple spatial scales, with several habitat‐specialist rare species bearing specific regenerative and resource‐use traits and relying on conditions threatened by climate change and land‐use intensification. Examining AORs through the lens of niche, functional traits and spatial scales enables mapping patterns and drivers of species commonness and rarity, enhancing understanding of species assembly and providing tools for biodiversity conservation.

ABSTRACT Understanding the factors governing grassland biodiversity across different spatial scales is crucial for effective conservation and management. However, most studies focus on single grain sizes, leaving the scale‐dependent mechanisms of biodiversity drivers unclear. We investigated how climate, soil properties, abiotic disturbance, and land use influence plant diversity across two fine spatial scales in various grassland types in Ukraine. Using spatially explicit data on plant species presence and their cover, collected at smaller (10 m2) and larger (100 m2) grain sizes, we assessed spatial β‐diversity—the variability of biodiversity between scales. We analyzed whether the effects of ecological drivers on β‐diversity are mediated by changes in species evenness, density (total cover), and intraspecific aggregation in plant community. In our study, the most influential factors of local plant diversity at both grain sizes were climate variables, followed by soil humus content, litter cover, and soil pH. Soil and litter effects were primarily driven by the response of locally rare species, while climate and grazing effects were driven by locally common species. The strength of most of these effects varied between spatial scales, affecting β‐diversity. Soil properties influenced β‐diversity through changes in total plant community cover, while the effects of climate and litter operated via changes in species evenness and aggregation. Our findings highlight that biodiversity responses to climate, soil factors, and litter depend on the size of the sampled area and reveal the role of total plant cover, evenness, and aggregation in driving fine‐scale β‐diversity in grasslands across different habitat types. Our findings support the idea that the responses of biodiversity to climate, soil factors, and litter depend on the size of the sampled area. We have identified main mechanisms behind the scale‐dependency of biodiversity drivers and highlighted the significance of locally rare and common species. Our results underscore the importance of incorporating the scale‐dependency of biodiversity drivers in conservation efforts, management strategies, and analyses of global change impacts, which would enhance our ability to predict potential biodiversity change.

When habitat use by field‐dwelling animals coincides in space and time with agricultural practices such as spring mowing of meadows, human‐wildlife conflicts can have deadly consequences for wildlife. Roe deer (Capreolus capreolus L.) fawns are particularly vulnerable because they hide in meadows during the rearing phase. Thus, a better understanding of the habitat drivers of bed‐site selection is critical to mitigating fawn mortality during mowing. Here, we tease apart the among‐field (presumably driven by maternal behaviour) and within‐field (driven by fawn behaviour) components of bed‐site selection of roe deer during the spring mowing season. We collected over 600 fawn bed sites across an environmentally diverse study region. At the among‐field scale, we implemented a used versus available design and employed a two‐part statistical model (GAMLSS) to identify habitat characteristics that were linked to either fawn presence (vs. absence) or abundance on a given field. At the within‐field scale, we compared habitat characteristics at fawn bed‐sites with paired random sites using a conditional logistic regression model. At the among‐field scale, fawns were more likely to be present, and were more abundant, in fields within more diverse, rural landscapes, with nearby woodland. Surprisingly, fawns were more often present in fields that were near roads and had lower vegetation productivity. At the within‐field scale, however, fawns preferred bed‐sites which were further from both roads and woodland, but that provided the best visual cover to minimise predation risk. Our findings revealed substantial and novel scale‐dependent differences in the drivers of habitat selection of mothers and fawns, which, together, determine the precise locations of bed‐sites between and within meadows. These results may aid wildlife managers in identifying areas where there is a high probability of encountering a roe deer fawn so as to initiate targeted searches prior to mowing and, ultimately, mitigate fawn mowing mortality. Grassland mowing is a major cause of mortality for roe deer fawns and a text‐book example for human‐wildlife conflicts due to the lack of information on factors that characterise bed‐sites. We differentiated between the maternal and neonatal behavioural components to explain the scale‐dependent biological drivers of bed‐site selection of roe deer with an innovative approach.

One of the few laws in ecology is that communities consist of few common and many rare taxa. Functional traits may help to identify the underlying mechanisms of this community pattern, since they correlate with different niche dimensions. However, comprehensive studies are missing that investigate the effects of species mean traits (niche position) and intraspecific trait variability (ITV, niche width) on species abundance. In this study, we investigated fragmented dry grasslands to reveal trait‐occurrence relationships in plants at local and regional scales. We predicted that (a) at the local scale, species occurrence is highest for species with intermediate traits, (b) at the regional scale, habitat specialists have a lower species occurrence than generalists, and thus, traits associated with stress‐tolerance have a negative effect on species occurrence, and (c) ITV increases species occurrence irrespective of the scale. We measured three plant functional traits (SLA = specific leaf area, LDMC = leaf dry matter content, plant height) at 21 local dry grassland communities (10 m × 10 m) and analyzed the effect of these traits and their variation on species occurrence. At the local scale, mean LDMC had a positive effect on species occurrence, indicating that stress‐tolerant species are the most abundant rather than species with intermediate traits (hypothesis 1). We found limited support for lower specialist occurrence at the regional scale (hypothesis 2). Further, ITV of LDMC and plant height had a positive effect on local occurrence supporting hypothesis 3. In contrast, at the regional scale, plants with a higher ITV of plant height were less frequent. We found no evidence that the consideration of phylogenetic relationships in our analyses influenced our findings. In conclusion, both species mean traits (in particular LDMC) and ITV were differently related to species occurrence with respect to spatial scale. Therefore, our study underlines the strong scale‐dependency of trait‐abundance relationships.

The patterns and underlying ecological (e.g. environmental filtering) and historical (e.g. priority effects) drivers of beta diversity are scale‐dependent, but generally difficult to distinguish and rarely explored with a sufficiently broad range of spatial scales. We propose a general scale‐explicit framework to assess and contrast the patterns and drivers of beta diversity across hierarchical spatial scales ranging from within fine‐scale ecoregion‐scale to among broad‐scale ecoregion‐scale. By applying this framework to aquatic macroinvertebrate datasets, we show that beta diversity generally increases with spatial extent. With an increasing spatial extent, beta diversity shifts from being more influenced by environmental filtering to being more influenced by recent historical factors (i.e. past beta diversity). Such recent historical effects may result from the past environmental variation rather than priority effects. We also found that the small‐scale and large‐scale environmental drivers act differently on beta diversity across spatial extents. Our research reveals a complex spatial‐scale dependence in beta diversity patterns and their drivers and provides a more holistic understanding of beta diversity dynamics. Our framework represents a flexible way to unravel the internal structure of beta diversity across scales by partitioning entire beta diversity variation into scale‐specific differences and may have broad application in community ecology, landscape planning and biodiversity conservation.

Aim: To determine the ecoregions (spatial marine areas with similar environmental and physical conditions associated with relatively homogeneous fish assemblages) for shallow reef fish assemblages based on predictive models of beta diversity (β-diversity) that account for both large-scale environmental factors and local habitat characteristics. We assessed the influence of a spatial scale to rank the importance of these factors. Location: New Caledonian (south-west Pacific Ocean, 17–24° S, 158–172° W) Exclusive Economic Zone, Coral Sea Marine Park. Taxon: Fish. Methods: Fish and habitat data that were collected at 13 sites around New Caledonia using unbaited rotating underwater video (285 sampling stations) were analysed. Gradient forest modelling was used to predict the fish β-diversity along the gradients of environmental factors. Ecoregions were obtained by applying clustering methods to gradient forest predictions. Results: The gradient forest models of β-diversity retained 59 species (total: 206 fish species) with R2 > 0, including 19 fish species with R2 from 0.03% to 69%. For these 19 species, the models explained up to 26% of the variance. At a large scale, β-diversity was significantly explained by nutrient concentrations, sea surface salinity and temperature. Among the eight ecoregions that were delineated based on the β-diversity predictions, three regions corresponded to remote sites under oceanic influence where human pressures are low and the surface nutrient concentrations are high. On the local scale, the benthic habitat explained β-diversity better than the physical and chemical parameters, particularly in the areas subject to anthropogenic pressures. Main conclusions: On the local scale, the respective importance of environmental factors (physical and chemical parameters versus benthic habitat) differed according to ecosystem health. Our findings suggest that nutrient enrichment due to avifauna may have a positive effect on fish β-diversity when an ecosystem is healthy. The ecoregions reflect fish species composition in relation to a large set of environmental parameters.

In the face of climate change, it is important to estimate changes in key ecosystem properties such as plant biomass and gross primary productivity (GPP). Ground truth estimates and especially experiments are performed at small spatial scales (0.01-1 m2) and scaled up using coarse scale satellite remote sensing products. This will lead to a scaling bias for non-linearly related properties in heterogeneous environments when the relationships are not developed at the same spatial scale as the remote sensing products. We show that unmanned aerial vehicles (UAVs) can reliably measure normalized difference vegetation index (NDVI) at centimeter resolution even in highly heterogeneous Arctic tundra terrain. This reveals that this scaling bias increases most at very fine resolution, but UAVs can overcome this by generating remote sensing products at the same scales as ecological changes occur. Using ground truth data generated at 0.0625 m2 and 1 m2 with Landsat 30 m scale satellite imagery the resulting underestimation is large (8.9%-17.0% for biomass and 5.0%-9.7% for GPP600) and of a magnitude comparable to the expected effects of decades of climate change. Methods to correct this upscaling bias exist but rely on sub-pixel information. Our data shows that this scale-dependency will vary strongly between areas and across seasons, making it hard to derive generalized functions compensating for it. This is particularly relevant to Arctic greening with a predominantly heterogeneous land cover, strong seasonality and much experimental research at sub-meter scale, but also applies to other heterogeneous landscapes. These results demonstrate the value of UAVs for satellite validation. UAVs can bridge between plot scale used in ecological field investigations and coarse scale in satellite monitoring relevant for Earth System Models. Since future climate changes are expected to alter landscape heterogeneity, seasonally updated UAV imagery will be an essential tool to correctly predict landscape-scale changes in ecosystem properties.

Understanding the mechanisms that drive the change of biotic assemblages over space and time is the main quest of community ecology. Assessing the relative importance of dispersal and environmental species selection in a range of organismic sizes and motilities has been a fruitful strategy. A consensus for whether spatial and environmental distances operate similarly across spatial scales and taxa, however, has yet to emerge. We used censuses of four major groups of organisms (soil bacteria, fungi, ground insects, and trees) at two observation scales (1-m² sampling point vs. 2,500-m² plots) in a topographically standardized sampling design replicated in two tropical rainforests with contrasting relationships between spatial distance and nutrient availability. We modeled the decay of assemblage similarity for each taxon set and site to assess the relative contributions of spatial distance and nutrient availability distance. Then, we evaluated the potentially structuring effect of tree composition over all other taxa. The similarity of nutrient content in the litter and topsoil had a stronger and more consistent selective effect than did dispersal limitation, particularly for bacteria, fungi, and trees at the plot level. Ground insects, the only group assessed with the capacity of active dispersal, had the highest species turnover and the flattest nonsignificant distance–decay relationship, suggesting that neither dispersal limitation nor nutrient availability were fundamental drivers of their community assembly at this scale of analysis. Only the fungal communities at one of our study sites were clearly coordinated with tree composition. The spatial distance at the smallest scale was more important than nutrient selection for the bacteria, fungi, and insects. The lower initial similarity and the moderate variation in composition identified by these distance-decay models, however, suggested that the effects of stochastic sampling were important at this smaller spatial scale. Our results highlight the importance of nutrients as one of the main environmental drivers of rainforest communities irrespective of organismic or propagule size and how the overriding effect of the analytical scale influences the interpretation, leading to the perception of greater importance of dispersal limitation and ecological drift over selection associated with environmental niches at decreasing observation scales.

QUESTIONS: Are the structural characteristics of natural beech forests predominant on small monitoring plots representative of the large‐scale features of these forests? Do the findings of our large‐scale investigation support the hypothesis that this primeval beech forest is shaped by fine‐scale processes, or is there evidence that high‐severity disturbance events have affected its structure? LOCATION: Ukrainian Carpathians, Uholka‐Shyrokyi Luh, the largest primeval beech forest in Europe, covering 102.8 km².METHODS: On 314 (500 m²) circular plots, systematically distributed across the forest, all living and dead trees with a DBH ≥6 cm were assessed. Lying deadwood, tree regeneration, the size of canopy gaps and the number of canopy layers were recorded. Spatial analyses were conducted using Moran's I. Dendrochronological analysis was used to reconstruct tree ages and growth patterns. RESULTS: The forest is characterized overall by a density of 435.0 ± 12.2 ha⁻¹(mean ± SE) living trees, a basal area of 36.6 ± 0.8 m²·ha⁻¹, a volume of living trees of 582.1 ± 13.5 m³·ha⁻¹and a total deadwood volume of 162.5 ± 8.4 m³·ha⁻¹. Beech is the dominant species (97.3 ± 0.7%, by basal area), interspersed with mostly deciduous species with moderate shade tolerance. The forest canopy is multi‐layered, with a high abundance of old trees, and with canopy gaps rarely larger than the crown projection area of a few trees. CONCLUSIONS: The results lend support to findings from small monitoring plots, but our landscape approach allows a reliable estimation of key forest characteristics such as basal area and standing volume, which tend to be overestimated in studies on subjectively placed small monitoring plots. We conclude that disturbance events of moderate or higher severity have left only few discernible traces in forest structure or species composition. All findings suggest that this forest is characterized by a mainly small‐scale disturbance regime, leading to a homogeneity of forest characteristics of the living stand at larger spatial scales and causing only minor fluctuations around the average values of forest parameters observed.

The precise determination of subsurface thermal properties is critical for ground-source heating systems. The geomaterials are inherently heterogeneous, and their thermal conductivity measured in laboratory and field tests often exhibits anisotropic behaviours. However, the accurate measurement of thermal responses in geomaterials presents a challenging task due to the anisotropy’s variation with the observed scale. Hence, a numerical method is developed in this work and illustrated by taking a typical anisotropic structure of geomaterials with the porosity of 0.5 as an example. The differences in data from laboratory measurements and field tests are discussed to explore the scale effect on anisotropic thermal properties. A series of simulation tests are conducted on specimens with varying dimensions using the finite element method. Results indicate that the thermal properties show a substantial sensitivity to the observation scale, the variation of which decreases with the sample dimensions. By comparing in situ data and laboratory results, the values of average thermal conductivity and corresponding anisotropy ratio are lower than those at small scales, indicating that careful consideration should be given to the thermal properties to account for heterogeneity and anisotropy. In addition, four upscaling schemes based on the averaging method are discussed. This study sheds light on the gap between the laboratory results and the field’s inherent properties and provides guidelines for upscaling small-scale results to field-scale applications.