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opencc-by-4.0Nov 2014View details →

Fig. 3 in Spatial Patterns Of Bird Communities Of The Lower Dnieper Sands During The Breeding Season: Differentiation Factors

Fig. 3. The abundance (the mean number of individuals per sample) of campophilous and dendrophilous birds in groups of samples A (Clusters I–IV) and B (Clusters V–VI). N o t e. The central line represents median, the lower and upper limits of the rectangle — the first and third quartile respectively, "whiskers" — ± 1.5 of interquartile range; circles — outliers.

opencc-by-4.0Nov 2014View details →

Fig. 1 in Spatial Patterns Of Bird Communities Of The Lower Dnieper Sands During The Breeding Season: Differentiation Factors

Fig. 1. The scheme of the study area. Аrenas of Low-Dnieper Sands: A — Kakhovska; B — Kozachelaherska; C — Oleshkivska; D — Chalbaska; E — Zburivska; F — Ivanivska; G — Kinburn Peninsula. N o t e. The first and the last sample of each census route are marked by numbers; the numbering of samples is the same as in table 1.

opencc-by-4.0Nov 2014View details →

Fig. 4 in Spatial Patterns Of Bird Communities Of The Lower Dnieper Sands During The Breeding Season: Differentiation Factors

Fig. 4. The area ratio of different types of habitats on standard test plots in the groups of samples.

opencc-zeroMay 2022View details →

Geographical variation in the trait-based assembly patterns of multitrophic invertebrate communities

It has been argued that the mechanisms structuring ecological communities may be more generalizable when based on traits than on species identities. If so, patterns in the assembly of community-level traits along environmental gradients should be similar in different places in the world. Alternatively, geographic change in the species pool and regional variation in climate might result in site-specific relationships between community traits and local environments. These competing hypotheses are particularly untested for animal communities. Here we test the geographic constancy of trait-based assembly patterns using a widespread multi-trophic community: aquatic macroinvertebrates within bromeliads. We used data on 615 invertebrate taxa from 1656 bromeliads in 26 field sites from Mexico to Argentina. We summarized invertebrate traits with four orthogonal axes, and used these trait axes to examine trait convergence and divergence assembly patterns along three environmental gradients: detrital biomass and water volume in bromeliads, and canopy cover over bromeliads. We found no overall signal of trait-based assembly patterns along any of the environmental gradients. However, individual sites did show trait convergence along detrital and water gradients, and we built predictive models to explore these site differences. Sites that showed trait convergence along detrital gradients were all north of the Northern Andes. This geographic pattern may be related to phylogeographic differences in bromeliad morphology. Bromeliads with low detritus were dominated by detritivorous collectors and filter feeders, where those with high detritus had more sclerotized and predatory invertebrates. Sites that showed the strongest trait convergence along gradients in bromeliad water were in regions with seasonal precipitation. In such sites, bromeliads with low water were dominated by soft-bodied, benthic invertebrates with simple life cycles. In less seasonal sites, traits associated with short-term desiccation resistance, such as hard exoskeletons, were more important. In summary, we show that there are strong geographic effects on the trait-based assembly patterns of this invertebrate community, driven by the biogeography of their foundational plant species as well as by regional climate. We suggest that inclusion of biogeography and climate in trait-based community ecology could help make it a truly general theory. (excerpted from Srivastava, DS et al. 2022. Geographical variation in the trait-based assembly patterns of multitrophic invertebrate communities. Functional Ecology)

opencc-zeroOct 2021View details →

Opposing community assembly patterns for dominant and non-dominant plant species in herbaceous ecosystems globally

Biotic and abiotic factors interact with dominant plants —the locally most frequent or with the largest coverage— and non-dominant plants differently, partially because dominant plants modify the environment where non-dominant plants grow. For instance, if dominant plants compete strongly, they will deplete most resources, forcing non-dominant plants into a narrower niche space. Conversely, if dominant plants are constrained by the environment, they might not exhaust available resources but instead may ameliorate environmental stressors that usually limit non-dominants. Hence, the nature of interactions among non-dominant species could be modified by dominant species. Furthermore, these differences could translate into a disparity in the phylogenetic relatedness among dominants compared to the relatedness among non-dominants. By estimating phylogenetic dispersion in 78 grasslands across five continents, we found that dominant species were clustered (e.g., co-dominant grasses), suggesting dominant species are likely organized by environmental filtering, and that non-dominant species were either randomly assembled or overdispersed. Traits showed similar trends for those sites (<50%) with sufficient trait data. Furthermore, several lineages scattered in the phylogeny had more non-dominant species than expected at random, suggesting that traits common in non-dominants are phylogenetically conserved and have evolved multiple times. We also explored environmental drivers of the dominant/non-dominant disparity. We found different assembly patterns for dominants and non-dominants, consistent with asymmetries in assembly mechanisms. Among the different postulated mechanisms, our results suggest two complementary hypotheses seldom explored: (1) Non-dominant species include lineages adapted to thrive in the environment generated by dominant species. (2) Even when dominant species reduce resources to non-dominant ones, dominant species could have a stronger positive effect on some non-dominants by ameliorating environmental stressors affecting them, than by depleting resources and increasing the environmental stress to those non-dominants. These results show that the dominant/non-dominant asymmetry has ecological and evolutionary consequences fundamental to understand plant communities.

opencc-zeroDec 2021View details →

Data from: Different taxonomic and functional indices complement the understanding of herb-layer community assembly patterns in a southern-limit temperate forest

The efficient conservation of vulnerable ecosystems in the face of global change requires a complete understanding of how plant communities respond to various environmental factors. We aim to demonstrate that a combined use of different approaches, traits, and indices representing each of the taxonomic and functional characteristics of plant communities will give complementary information on the factors driving vegetation assembly patterns. We analyzed variation across an environmental gradient in taxonomic and functional composition, richness, and diversity of the herb-layer of a temperate beech-oak forest that was located in northern Spain. We measured species cover and four functional traits: leaf dry matter content (LDMC), specific leaf area (SLA), leaf size, and plant height. We found that light is the most limiting resource influencing herb-layer vegetation. Taxonomic changes in richness are followed by equivalent functional changes in the diversity of leaf size but by opposite responses in the richness of SLA. Each functional index is related to different environmental factors even within a single trait (particularly for LDMC and leaf size). To conclude, each characteristic of a plant community is influenced by different and even contrasting factors or processes. Combining different approaches, traits, and indices simultaneously will help us understand how plant communities work.

opencc-zeroSep 2022View details →

Data and code from: Functional rarity of plants in German hay meadows - patterns on the species level and mismatches with community species richness

Functional rarity (FR) - a feature combining a species' rarity with the distinctiveness of its traits - represents a promising tool to better understand the ecological importance of rare species and consequently to protect functional diversity more efficiently. Yet, we lack a systematic understanding of FR on both the species level (which species are functionally rare and why) and the community level (how is FR associated with biodiversity and environmental conditions). Here, we quantify FR for 218 plant species from German hay meadows on a local, regional, and national scale by combining data from 6500 vegetation relevés and 15 ecologically relevant traits. We investigate the association between rarity and trait distinctiveness on different spatial scales via correlation measures and show which traits lead to low or high trait distinctiveness via distance-based redundancy analysis. We test how species richness and FR are correlated and use boosted regression trees to determine environmental conditions driving species richness and FR. On the local scale, only rare species showed high trait distinctiveness while on larger spatial scales rare and common species showed high trait distinctiveness. As infrequent trait attributes (e.g., legumes, low clonality) led to higher trait distinctiveness, we argue that functionally rare species are either specialists or transients. While specialists occupy a particular niche in hay meadows leading to lower rarity on larger spatial scales, transients display distinct but maladaptive traits resulting in high rarity across all spatial scales. More functionally rare species than expected by chance occurred in species-poor communities indicating that they prefer environmental conditions differing from characteristic conditions of species-rich hay meadows. Finally, we argue that functionally rare species are not necessarily relevant for nature conservation, since many were transients from surrounding habitats. Yet, FR can facilitate our understanding of why species are rare in a habitat and under which conditions these species occur.

Figure S4 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel

Figure S4. – Spatial-temporal correlation matrix at a 782 km2 (A) and 1043 km2 (B) scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).

Figure S2 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel

Figure S2. – Spatial hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).

Figure 2 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel

Figure 2. – Spatial correlation matrix at a 522 km2 scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).

Figure 11 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel

Figure 11. – Scophthalmus rhombus from low (blue) to high (red) median densities of numbers/ km2 in log scale for 522 km2 for the Eastern English Channel.

Figure S5 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel

Figure S5. – Spatial-temporal hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).

Fig. 7 in First Records and Community Pattern of Arcellinida Inhabiting a Pristine and Remote Island from Southeastern Pacific, Chile

Fig. 7. Bubble plots of the two most conspicuous species making up the observed similarity within each community. For community A: A – Cyclopyxis arcelloides; B – Difflugia oblonga curvicollis; and for community B: C – Argynnia dentistoma; D – Apodera vas. Bubble plots are superimposed from the nMDS showed in Fig. 5. Bubble size approximates relative proportion of a given species in each sampling sites (gray circles with numbers) and each community type (outlined circles).

Fig. 8 in First Records and Community Pattern of Arcellinida Inhabiting a Pristine and Remote Island from Southeastern Pacific, Chile

Fig. 8. Bubble plots of the two most conspicuous species making up the observed dissimilarity between both communities: A – Certesella martiali and B – Difflugia globularis. Bubble plots are superimposed from the nMDS showed in Fig. 5. Bubble size approximates relative proportion of a given morphospecies in each sampling sites (gray circles with numbers) and each community type (outlined circles).

Fig. 3 in First Records and Community Pattern of Arcellinida Inhabiting a Pristine and Remote Island from Southeastern Pacific, Chile

Fig. 3. Species observed on the Guamblin Island. 1 – Centropyxis aculeata aculeata; 2 – C. aculeata oblonga; 3 – C. aerophila; 4 – C. discoides; 5 – C. elongata; 6 – C. constricta; 7a, b – Cyclopyxis arcelloides; 8a, b – C. eurystoma; 9a, b – C. intermedia; 10a, b – C. kahli; 11a, b – Plagiopyxis glyphostoma major; 12 – Difflugia lanceolata; 13 – D. cylindrus; 14 – D. mitriformis; 15 – D. globularis; 16 – Pontigulasia compressa c.f.; 17 – D. lata; 18 – D. oblonga curvicollis c.f.; 19 – D. bryophila; 20 – Apodera vas; 21 – Certesella certesi; 22 – Heleopera sphagni; 23 – H. petricola; 24 – Argynnia dentistoma; 25 – A. vitrea; 26 – Nebela barbata psilonata; 27 – N. penardiana; 28 – N. collaris; 29 – Padaungiella (Nebela) lageniformis. The background of SEM images were retouched in some cases to highlight the organisms, however, the microorganisms per se were not manipulated in any way.

Fig. 5 in First Records and Community Pattern of Arcellinida Inhabiting a Pristine and Remote Island from Southeastern Pacific, Chile

Fig. 5. Bracketed samples encompass groupings (i.e. communities) that are dissimilar from other sampling sites at 43.69% (p = 0.001). A – Community A, includes those sampling sites located outside the forest (treeless-group); and B – Community B, includes those sampling sites located within the forest and at the forest margins (forested-group). Groupings are according to the group average method on Bray– Curtis similarity index and fourth root transformed abundance-data. Black solid lines correspond to significant clusters and dotted lines correspond to clusters without significant internal structure (based on SIMPER analysis using 1,000 permutations).

Fig. 6 in First Records and Community Pattern of Arcellinida Inhabiting a Pristine and Remote Island from Southeastern Pacific, Chile

Fig. 6. Non-metric multidimensional scaling plot (nMDS) and Kruskal stress value for the nMDS configuration based on the abundance data of testate amoebae species found along the surveyed sampling sites on the Guamblin Island. Outlined circles represent groupings (i.e. communities) that are dissimilar from other sampling sites at 43.69% (based on SIMPER analysis using 1,000 permutations, p = 0.001). Groupings are according to the group average method on Bray–Curtis similarity index and fourth root transformed abundance-data.

opencc-by-4.0Dec 2005View details →

Fig. 5 in Patterns Of Polychaete Communities In Tropical Sedimentary Habitats: A Case Study In South-Western Thailand

Fig. 5. Multivariate analysis output using presence/absence transformed polychaete data: (a) MDS plot showing all sites arranged by habitat, (b) MDS plot excluding Exposed Beach habitat. Habitat abbreviations as in Fig. 2.

opencc-by-4.0Dec 2005View details →

Fig. 2 in Patterns Of Polychaete Communities In Tropical Sedimentary Habitats: A Case Study In South-Western Thailand

Fig. 2. Mean density (SE) for polychaetes and macrofauna at each sampled habitat. EB = Exposed Beach, NV = Non-vegetated sediment, SG = Seagrass.