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Fig. 3 in Spatial variation of dung beetle assemblages associated with forest structure in remnants of southern Brazilian Atlantic Forest
Fig. 3. Multivariate Regression Tree (MRT) analysis of the dung beetle abundance (a) and biomass with environmental variables as predictor variables. Environmental variables: A, basal area of trees; I, land slope; J, altitude; N, height of leaf litter; O, canopy cover. Species names are abbreviated and can be found in legend of Fig. 4 and in Appendix C. Bar charts at the terminal leaves of the regression tree represent species abundance means. The order of bars in each bar chart are the same and follows the sequence of names showed at left.
Fig. 1 in Spatial variation of dung beetle assemblages associated with forest structure in remnants of southern Brazilian Atlantic Forest
Fig. 1. Map of the Atlantic Forest remnants where dung beetles were sampled during January and February 2012. Anhatomirim Environmental Protection Area in Governador Celso Ramos city; Permanent Protection Areas of Itapema city; Peri Lagoon Municipal Park in Florianópolis city; Permanent Protection Areas of Ratones in Florianópolis city.
Figure 4 in Organic farming and moderate tillage change the dominance and spatial structure of soil Collembola communities but have little effects on bulk abundance and species richness
Figure 4. Spatial random effects explaining abundance, number of species and Berger-Parker index in samples in different management types. Grayscale palette shows scales (meters, 25cm-plots, 7-cm plots). Barplots show Tau parameter of the (spatial) random effects; mixed-effects models were run in each management type separately.
Figure 1 in Organic farming and moderate tillage change the dominance and spatial structure of soil Collembola communities but have little effects on bulk abundance and species richness
Figure 1. Location of the fields studied with different treatments: ODISK (Organic farming and Disking - C and D circles), OTILL (Organic farming and Tillage - A and Bsquares), and CONV (Conventional farming - E and F triangles). Coordinates of locations: A: N 54.482 E 34.928, B: N 54.496 E 34.933, C: N 54.543 E 34.880, D: N 54.555 E 34.996, E: N 54.585 E 35.0187, F: N 54.570 E 34.974. The altitude was from 175 to 234 m above sea level depending on the field.
Figure 2 in Organic farming and moderate tillage change the dominance and spatial structure of soil Collembola communities but have little effects on bulk abundance and species richness
Figure 2. Spatially nested hierarchical sampling design. In total, 486 samples were collected from 6 fields across 3 management types.
Fig. 3 in Planktonic Ciliates of the Neva Estuary (Baltic Sea): Community Structure and Spatial Distribution
Fig. 3. Distribution patterns of ciliate abundance (ind ml–1, dark bars) and biomass (× 10–3 µg C ml–1, empty bars) in the Neva Estuary.
Fig. 4 in Planktonic Ciliates of the Neva Estuary (Baltic Sea): Community Structure and Spatial Distribution
Fig. 4. Relative abundance (%) of different size groups of ciliates in the Neva Estuary. Data are not presented for four stations (indicated by points) with extremely low ciliate abundances (<0.1 ind ml–1).
Figure 2 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 2. Vertical distribution of temperature, salinity, dissolved oxygen, and chlorophyll a concentrations in the central and western Bay of Bengal during spring intermonsoon season.
Figure 15 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 15. Microphotographs of copepods Pleuromamma indica (A), P. xiphias (B), Gaetanus kruppii (C), and Gaussia princeps (D) from the 200–300 m stratum (oxygen minimum zone) between CB3 and CB5. The scale bar is in millimeters.
Figure 7 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 7. Distribution of dominant (>1%) zooplankton taxonomic groups in different depth strata in the central (a) and western (b) Bay of Bengal during spring intermonsoon. The percentages at every depth are averages from 5 stations in the central and 4 stations in the western bay.
Data from: The importance of landscape and spatial structure for hymenopteran-based food webs in an agro-ecosystem
1. Understanding the environmental factors that structure biodiversity and food webs among communities is central to assess and mitigate the impact of landscape changes. 2. Wildflower strips are ecological compensation areas established in farmland to increase pollination services and biological control of crop pests, and to conserve insect diversity. They are arranged in networks in order to favour high species richness and abundance of the fauna. 3. We describe results from experimental wildflower strips in a fragmented agricultural landscape, comparing the importance of landscape, of spatial arrangement, and of vegetation on the diversity and abundance of trap-nesting bees, wasps and their enemies, and the structure of their food webs. 4. The proportion of forest cover close to the wildflower strips and the landscape heterogeneity stood out as the most influential landscape elements, resulting in a more complex trap nest community with higher abundance and richness of hosts, and with more links between species in the food webs and a higher diversity of interactions. We disentangled the underlying mechanisms for variation in these quantitative food-web metrics. 5. We conclude that in order to increase the diversity and abundance of pollinators and biological control agents and to favour a potentially stable community of cavity nesting hymenoptera in wildflower strips, more investment is needed in the conservation and establishment of forest habitats within agro-ecosystems, as a reservoir of beneficial insect populations.
Data from: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non-causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross-validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non-random and blocked cross-validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross-validation is nearly universally more appropriate than random cross-validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross-validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.
Data from: Environmental conditions affect spatial genetic structures and dispersal patterns in a solitary rodent
The study of the spatial distribution of relatives in a population under contrasted environmental conditions provides critical insights into the flexibility of dispersal behaviour and the role of environmental conditions in shaping population relatedness and social structure. Yet few studies have evaluated the effects of fluctuating environmental conditions on relatedness structure of solitary species in the wild. The aim of this study was to determine the impact of interannual variations in environmental conditions on the spatial distribution of relatives [spatial genetic structure (SGS)] and dispersal patterns of a wild population of eastern chipmunks (Tamias striatus), a solitary rodent of North America. Eastern chipmunks depend on the seed of masting trees for reproduction and survival. Here, we combined the analysis of the SGS of adults with direct estimates of juvenile dispersal distance during six contrasted years with different dispersal seasons, population sizes and seed production. We found that environmental conditions influences the dispersal distances of juveniles and that male juveniles dispersed farther than females. The extent of the SGS of adult females varied between years and matched the variation in environmental conditions. In contrast, the SGS of males did not vary between years. We also found a difference in SGS between males and females that was consistent with male-biased dispersal. This study suggests that both the dispersal behaviour and the relatedness structure in a population of a solitary species can be relatively labile and change according to environmental conditions.
Data from: Fine-scale spatial genetic structure across the species range reflects recent colonization of high elevation habitats in silver fir (Abies alba Mill.)
<p class="western"><span>Variation in genetic diversity across species ranges has long been recognized as highly informative for assessing populations' resilience and adaptive potential. The spatial distribution of genetic diversity within populations, referred to as fine-scale spatial genetic structure (FSGS), also carries information about recent demographic changes, yet it has rarely been connected to range scale processes. We studied eight silver fir (<i>Abies alba </i>Mill.<i>)</i> population pairs (sites), growing at high and low elevations, representative of the main genetic lineages of the species. A total of 1368 adult trees and 540 seedlings were genotyped using 137 and 116 single nucleotide polymorphisms (SNPs), respectively. Sites revealed a clear east-west isolation-by-distance pattern consistent with the post-glacial colonization history of the species. Genetic differentiation among sites (<i>F</i><sub>CT</sub>=0.148) was an order of magnitude greater than between elevations within sites (<i>F</i><sub>SC</sub>=0.031), nevertheless high elevation populations consistently exhibited a stronger FSGS. Structural equation modeling revealed that elevation and, to a lesser extent, post-glacial colonization history, but not climatic and habitat variables, were the best predictors of FSGS across populations. These results suggest that high elevation habitats have been colonized more recently across the species range. Additionally, paternity analysis revealed a high reproductive skew among adults and a stronger FSGS in seedlings than in adults, suggesting that FSGS may conserve the signature of demographic changes for several generations. Our results emphasize that spatial patterns of genetic diversity within populations provide information about demographic history complementary to non-spatial statistics, and could be used for genetic diversity monitoring, especially in forest trees.</span></p>
Data from: Grazing and nitrogen addition restructure the spatial heterogeneity of soil microbial community structure and enzymatic activities
<p>1. In grassland ecosystems, large herbivorous animal grazing activity and increasing nitrogen deposition strongly alters microbial community structure and function. Understanding the effects of grazing and nitrogen addition on the spatial heterogeneity in soil microbial community structure, enzymatic activities and the underlying mechanisms are crucial for making better predictions of soil organic matter dynamics and nutrient cycling. </p> <p>2. We examined the spatial heterogeneity of soil microbial community structure and enzymatic activity associated with changes in soil microclimate, soil characteristics, plant biomass and soil nutrient responses to grazing and nitrogen addition using a manipulative experiment with control (CK), grazing (G), nitrogen addition (N) and grazing plus nitrogen addition (NG) treatments in a <i>Leymus chinensis </i>meadow steppe, in northeastern China. </p> <p>3. The results demonstrated that soil microbial community structure and enzymatic activities showed a high level of spatial dependence [C/(C + C0)≥0.9] in the CK plot. G, N and NG treatments not only reduced the spatial variability ofsoil microbial community structure and enzymatic activities, but also reshaped the spatial links between enzymes activities and microbial community structure. Litter biomass, soil temperature and soil nutrients (soil dissolved inorganic nitrogen or soil dissolved organic carbon) explained 21-27% of the spatial variability of soil microbial community structure in the CK treatment and pH was the strongest driver for the spatial variability of soil enzymatic activities. Meanwhile, the homogenization in soil water content induced by the N addition treatment was a determinant of the reduction in spatial heterogeneity of the microbial community structure. The combination of soil physicochemical properties (bulk density, soil pH and soil dissolved inorganic nitrogen), soil temperature and root biomass explained 32-43% of the spatial variability of the microbial community structure in the G treatment, and N and G treatments had additive effects on the spatial heterogeneity of total PLFAs by homogenizing root biomass. Plant biomass and microbial community structure were the major drivers for the spatial heterogeneity of enzymatic activities under G, N and NG. In NG, the change in spatial variability of enzymatic activities was dominated by N addition. Regardless of grazing, N addition facilitated the spatial correlation between microbial community structure and enzyme activities. </p> <p>4. Overall, our results revealed the drivers of soil microbial community structure and enzymatic activities spatial pattern shift due to grazing and N addition, highlighting the role that spatial variability in soil microbial community structure and enzymatic activities has on the <i>L. chinensis</i> meadow steppe.</p>
figure 5 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 5 Cumulative current map, based on all possible pairs of sampling locations, representing the amount of current flowing through each pixel. Higher current flow represents higher connectivity, and vice versa.
figure 2 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 2 Definition of the six main river basins by drawing a buffer area of 1 km around all waterways connected to the main rivers: in green, the Cilento basin; in pink, the Agri basin; in blue, the Sinni basin; in red, the Lao basin; in orange, the Basento basin; in violet, the Abatemarco basin. Red spots indicate the location of the collected samples. The bold blue lines highlight the main rivers, while the tiny blue lines show all other waterways.
Data from: Spatial and spatiotemporal variation in metapopulation structure affects population dynamics in a passively dispersing arthropod
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Data from: Grazing and nitrogen addition restructure the spatial heterogeneity of soil microbial community structure and enzymatic activities
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Data from: Carryover effects drive competitive dominance in spatially structured environments
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Allen Brain Atlas
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