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508 results for “Ecosystem functions”
Data from: Studying biodiversity-ecosystem function relationships in experimental microcosms among islands
<p>Ecological studies on islands have provided fundamental insights into the mechanisms underlying biodiversity of larger organisms, but we know little about the factors affecting island microbial biodiversity and ecosystem function. We conducted a field experiment on five Baltic Sea islands where we placed aquatic microcosms with different levels of salinity mimicking environmental stress and allowed diatoms to colonize the microcosms via air. Using structural equation models (SEM), we investigated the interconnections among environmental and dispersal-related factors, diatom biodiversity, and ecosystem productivity (represented by chlorophyll a concentration). We also tested whether the body size structure of the community influences productivity together with biodiversity. In SEMs, we found no relationship between species richness or evenness and productivity. Rather, productivity increased with increasing mean body size of species in the communities. The effects of environmental stress on both biodiversity and ecosystem productivity were highlighted as species richness and evenness declined, whereas productivity increased at the highest salinity levels. In addition to salinity, wind exposure affected both biodiversity metrics and productivity. This study provides new insights into microbial community assembly in a field experimental setting and the relationship between biodiversity and ecosystem function. Our results indicate that salinity presents a strong abiotic filter, leading to communities that may be species-poor, yet comprise salinity-tolerant and relatively productive species at high salinity. Our findings also emphasize the importance of mean community body size in mediating the effects of environmental conditions on productivity and suggest that this trait should be considered more broadly in biodiversity-ecosystem function studies.</p>
Figure 3 from: Datry T, Allen D, Argelich R, Barquin J, Bonada N, Boulton A, Branger F, Cai Y, Cañedo-Argüelles M, Cid N, Csabai Z, Dallimer M, de Araújo JC, Declerck S, Dekker T, Döll P, Encalada A, Forcellini M, Foulquier A, Heino J, Jabot F, Keszler P, Kopperoinen L, Kralisch S, Künne A, Lamouroux N, Lauvernet C, Lehtoranta V, Loskotová B, Marcé R, Martin Ortega J, Matauschek C, Miliša M, Mogyorósi S, Moya N, Müller Schmied H, Munné A, Munoz F, Mykrä H, Pal I, Paloniemi R, Pařil P, Pengal P, Pernecker B, Polášek M, Rezende C, Sabater S, Sarremejane R, Schmidt G, Senerpont Domis L, Singer G, Suárez E, Talluto M, Teurlincx S, Trautmann T, Truchy A, Tyllianakis E, Väisänen S, Varumo L, Vidal J-P, Vilmi A, Vinyoles D (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes 7: e77750. https://doi.org/10.3897/rio.7.e77750
Figure 3 The DRYvER three-step workflow embedded within 7 Work Packages (WP) and the four main attributes of the DRYvER consortium (red ovals).
Figure 2 from: Datry T, Allen D, Argelich R, Barquin J, Bonada N, Boulton A, Branger F, Cai Y, Cañedo-Argüelles M, Cid N, Csabai Z, Dallimer M, de Araújo JC, Declerck S, Dekker T, Döll P, Encalada A, Forcellini M, Foulquier A, Heino J, Jabot F, Keszler P, Kopperoinen L, Kralisch S, Künne A, Lamouroux N, Lauvernet C, Lehtoranta V, Loskotová B, Marcé R, Martin Ortega J, Matauschek C, Miliša M, Mogyorósi S, Moya N, Müller Schmied H, Munné A, Munoz F, Mykrä H, Pal I, Paloniemi R, Pařil P, Pengal P, Pernecker B, Polášek M, Rezende C, Sabater S, Sarremejane R, Schmidt G, Senerpont Domis L, Singer G, Suárez E, Talluto M, Teurlincx S, Trautmann T, Truchy A, Tyllianakis E, Väisänen S, Varumo L, Vidal J-P, Vilmi A, Vinyoles D (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes 7: e77750. https://doi.org/10.3897/rio.7.e77750
Figure 2 It shows how DRYvER will use this cyclic model as a structured loop embedded in a meta-system perspective to guide adaptive management of DRNs. DRYvER will translate climate projections into changes in flow intermittence patterns at multiple scales, including that of the strategically-selected focal DRNs. This physical setting will then be used to implement a dynamic meta-system perspective to understand the cascading changes in biodiversity, ecosystem functions and ecosystem services. This knowledge will be integrated to develop a multi-criteria decision framework combining scientific, management, socio-economic, legislative barriers and leverages to promote an adaptive management of DRNs.
Figure 4 from: Datry T, Allen D, Argelich R, Barquin J, Bonada N, Boulton A, Branger F, Cai Y, Cañedo-Argüelles M, Cid N, Csabai Z, Dallimer M, de Araújo JC, Declerck S, Dekker T, Döll P, Encalada A, Forcellini M, Foulquier A, Heino J, Jabot F, Keszler P, Kopperoinen L, Kralisch S, Künne A, Lamouroux N, Lauvernet C, Lehtoranta V, Loskotová B, Marcé R, Martin Ortega J, Matauschek C, Miliša M, Mogyorósi S, Moya N, Müller Schmied H, Munné A, Munoz F, Mykrä H, Pal I, Paloniemi R, Pařil P, Pengal P, Pernecker B, Polášek M, Rezende C, Sabater S, Sarremejane R, Schmidt G, Senerpont Domis L, Singer G, Suárez E, Talluto M, Teurlincx S, Trautmann T, Truchy A, Tyllianakis E, Väisänen S, Varumo L, Vidal J-P, Vilmi A, Vinyoles D (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes 7: e77750. https://doi.org/10.3897/rio.7.e77750
Figure 4 DRYvER focal DRNs located in highly contrasted EU and CELAC biogeographic and climatic settings (red points), chosen to span the expected natural variability of drying processes and associated DRN responses.Mediterranean ecoregion: the Genal network, in Andalucía (Spain, Mediterranean climate), a dry region heavily impacted by climate change, where most rivers are already affected by drying (contact partner: UB);Alpine ecoregion: the Albarine network, in the Southern Jura (France, temperate climate), a region mildly impacted by climate change (the Albarine network is part of a national LTER project and monitored since 2006) (contact partner: INRAE);Continental ecoregion: the Velička network, in Morava (Czech Republic, continental climate), a region heavily impacted by climate change where many perennial rivers are shifting towards intermittent flow (contact partner: MU);Balkanic ecoregion: the Krka network, in the Dinaric Karst (Croatia, Mediterranean climate), a region where most rivers are already drying and heavily impacted by climate change (contact partner: UZ);Pannonian ecoregion: the Bükkösdi-víz network, in the Mecsek (Hungary, continental climate), a region moderately impacted by climate change, where DRNs are becoming common (contact partner: UP);Boreal ecoregion: the Vantaanjoki network, Helsinki-Uusimaa Region (Finland, boreal climate), region moderately impacted by climate change, where flow intermittence is currently rare (contact partner: SYKE);Pacific Lowlands: the Cube network, in the Andean-Choco region (Ecuador, tropical climate), a region where drying is very seasonal and increasing in duration and frequency (contact partner: USFQ);Central High Andes ecoregion: the Rio Chico network in the Sucre region (Bolivia, semi-arid climate), a dry area prone to desertification where political conflicts emerge due to water scarcity (contact partner: USFX);Caatinga ecoregion: the Jaguaribe network, in the Northeast Semiarid region (Brazil, semi-arid climate), the driest region in Brazil, already heavily impacted by climate change (contact partner: UFC).
Figure 1 from: Datry T, Allen D, Argelich R, Barquin J, Bonada N, Boulton A, Branger F, Cai Y, Cañedo-Argüelles M, Cid N, Csabai Z, Dallimer M, de Araújo JC, Declerck S, Dekker T, Döll P, Encalada A, Forcellini M, Foulquier A, Heino J, Jabot F, Keszler P, Kopperoinen L, Kralisch S, Künne A, Lamouroux N, Lauvernet C, Lehtoranta V, Loskotová B, Marcé R, Martin Ortega J, Matauschek C, Miliša M, Mogyorósi S, Moya N, Müller Schmied H, Munné A, Munoz F, Mykrä H, Pal I, Paloniemi R, Pařil P, Pengal P, Pernecker B, Polášek M, Rezende C, Sabater S, Sarremejane R, Schmidt G, Senerpont Domis L, Singer G, Suárez E, Talluto M, Teurlincx S, Trautmann T, Truchy A, Tyllianakis E, Väisänen S, Varumo L, Vidal J-P, Vilmi A, Vinyoles D (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes 7: e77750. https://doi.org/10.3897/rio.7.e77750
Figure 1 Phases of flowing and drying alternate annually in the naturally intermittent Albarine River (France), a focal DRN of DRYvER. About half of EU's river channels now flow intermittently and this fraction is increasing. Photos: T. Datry.
Biomassess of functional groups and ecosystem parameters predicted by ecosim models of Lake Dianchi
<p>Understanding the relative importance of multiple stressors is valuable to prioritize conservation and restoration measures. Yet, the effects of multiple stressors on ecosystem functioning remain largely unknown in many freshwaters. Here, we provided a methodology combining ecosystem modelling with linear regression to disentangle the effects of multiple stressors on matter flow, an important ecosystem function. Treating a shallow lake as the model ecosystem, we simulated matter flow dynamics during 1950s-2010s with different combinations of stressors by Ecopath with Ecosim (EwE) modelling, and determined the relative importance of each stressor by generalized linear mixed models. We found that matter flow of the lake food web was highly dynamic, attributing to effects of multiple anthropogenic stressors. Biological invasion played the strongest role in driving the matter flow dynamics, followed by eutrophication, while biomanipulation (i.e. phytoplankton control by planktivorous fish stocking) was of little importance. Eutrophication had a stronger role on primary producers, pelagic food chain and top predators, while biological invasion on consumers in the middle of food chains. The former was more important in driving the quantity of matter flow, while the latter on trophic transfer efficiencies. Scenario forecasting showed that reducing nutrients contents could largely shape the matter flow pattern, while biomanipulation had little effect. Our findings provided new insights into understanding the mechanistic links between anthropogenic stressors and ecosystem functioning by combining ecosystem modelling with linear regression.</p>
Thessaloniki's urban waterfront: decision-making and governmental aspects for adding metropolitan ecosystem functions
Open the record for dataset details and reuse information.
FIGURE 9 in Synchrotron-radiation computed tomography uncovers ecosystem functions of fly larvae in an Eocene forest
FIGURE 9. CT scans of the fecal matter in the AKBS-0030 amber piece. Fly larvae are marked with the arrows, while the rest objects in the matrix of the amber are the pieces of the fecal matter. A, lower part of amber piece; B, frontal view on the amber piece; C and D, close up on the plant remnants in the fecal matter. Abbreviations: ct - cuticle; ep - epiderma; ph- phloem; par- parenchyma; scl - sclerenchyma; xyl - xylem.
FIGURE 6 in Synchrotron-radiation computed tomography uncovers ecosystem functions of fly larvae in an Eocene forest
FIGURE 6. Larvae of the group Volucellini (Syrphidae) from the Baltic amber, renders of a SR-µCT scan. A-E, Dip- 00897; F-I, Dip-00896. A, lateral view, render of a SR-µCT scan; B, Head, sagittal slice, with internal head skeleton marked in orange; C, Cephalo-pharyngeal skeleton, dorsal view; D, same, lateral view; E, same, ventral view; F, lateral view; G, frontal slice, through the head and thorax; H, Lateral slice, with well visible oesophagus, mandible marked in orange; I, lateral slice through the head; mandible marked in orange. Abbreviations: hp - hypopharynx; mp - metacephalic plate; md - mandibular hooks; hd - head; an - antennae, es - esophagus.
FIGURE 3 in Synchrotron-radiation computed tomography uncovers ecosystem functions of fly larvae in an Eocene forest
FIGURE 3. Diversity of fly larvae in Baltic amber. Optical images. A, AKBS-0030, full amber piece, arrow is pointing to the location of the inset from Fig. 3B; B, Cyclorrhapha, morphotype 1 close-up; C, PED-230, Athericidae, dorsal view; D, Heleomyzidae, puparium, Dip-00890, dorsal view; E, Volucellini, Dip-00889, lateral; F, Dip-00898, Chamaemyiidae, head in dorsal view; G, Dip-00892, Cyclorrhapha, morphotype 2, lateral view; H, Dip-00888, Syrphidae, Volucellini lateral view; I, Dip-00896, Volucellini, lateral view.
FIGURE 1 in Synchrotron-radiation computed tomography uncovers ecosystem functions of fly larvae in an Eocene forest
FIGURE 1. Diversity of fly larvae in Baltic amber. A, B, PED-230, Athericidae. Fringed lobes on the trunk end. B, Pseudopods with claws; C–E, Dip-00898, Chamaemyiidae, C, lateral view, head, anterior spiracle is marked with an arrow, render of SR-µCT scan; D, posterior spiracles openings are marked with arrows, render of SR-µCT scan; E, dorsal view, with clearly visible secondary annulation of the trunk; F, SMF-BE-10616, Chamaemyiidae, dorsal view; G, same, posterior spiracles, dorsal view; H, SMF-BE-10726, Phoridae representative puparium, dorsal view; I, same, head, dorsal view; J, same, trunk end, dorsal view.
FIGURE 2 in Synchrotron-radiation computed tomography uncovers ecosystem functions of fly larvae in an Eocene forest
FIGURE 2. Diversity of fly larvae in Baltic amber. All volume renders based on SR-µCT scans A, PED-230, Athericidae, dorsal; B, same, lateral view; C, D, Chamaemyiidae; C, Dip-00898, dorsal view; D, Dip-00898, head in dorsal view; E, BI-2356, puparium Cyclorrhapha, morphotype 1, lateral view; F, Dip-00888, Syrphidae, Volucellini, dorsal view; G, same, ventral view; H–K, Cyclorrhapha. H–I, Morphotype 2; H, Dip-00892, sagittal slice; I, Dip-00892, lateral view; I, Morphotype 3; BI2354, lateral view; J, Morphotype 3; BI2354, lateral view; K, Dip-00893, lateral view.
FIGURE 5. Volucella bombylans L in Synchrotron-radiation computed tomography uncovers ecosystem functions of fly larvae in an Eocene forest
FIGURE 5. Volucella bombylans L. extant larva for comparison, from ZSM collection, collected at Ober-Bayern, Kiefersfelden, from wasp nest on the house leg. Seggmann. A, head, laterally; B, anterior spiracle; C, dorsal view; D, ventral view; E, lateral view; F, Posterior spiracle.
FIGURE 8 in Synchrotron-radiation computed tomography uncovers ecosystem functions of fly larvae in an Eocene forest
FIGURE 8. Diversity of fly larvae in Baltic amber. All representative of Cyclorrhapha, A, morphotype 1, AKBS-0030, lateral view, render of a SR-µCT scan; B, same, sagittal slice of the head, render of a SR-µCT scan; C, Dip-00893, morphotype 3, dorsal view, render of a SR-µCT scan; D, Dip-00893, morphotype 3, head, dorsal view, render of a SR-µCT scan; E, Dip-00893 morphotype 3, head, dorsal view, render of a SR-µCT scan; E, Dip-00893, morphotype 3, posterior spiracles, ventral view, render of a SR-µCT scan; F, Dip-00893, morphotype 3, lateral view, render of a SR-µCT scan; G, Dip-00892, morphotype 2, sagittal slice through the head, render of a synchrotron scan; H, same, lateral view, ren- der of a synchrotron scan; I, morphotype 4, SMF-BE-10645, ventrolateral view; J, same, head.
Figure 1a from: Eisenhauer N (2018) Aboveground-belowground interactions drive the relationship between plant diversity and ecosystem function. Research Ideas and Outcomes 4: e23688. https://doi.org/10.3897/rio.4.e23688
Figure 1a - Importance of the duration of the experiment for its outcome. <br> The effect of plant diversity on plant productivity and on the performance of decomposers increases over time. Regression between the R² of the relationship between plant diversity and plant productivity and the R² of the relationship between plant diversity and decomposer biomass/density. Data from the Jena Experiment from different years [plant productivity in 2003 – 2009; microbial biomass in 2003 – 2009 (white circles); meso- (gray circles) and macroinvertebrate densities (black circles) in 2004, 2006 and 2008].
Figure 3 from: Eisenhauer N (2018) Aboveground-belowground interactions drive the relationship between plant diversity and ecosystem function. Research Ideas and Outcomes 4: e23688. https://doi.org/10.3897/rio.4.e23688
Figure 3 - Plant diversity effects on soil microbes more pronounced at elevated [CO2]. Microbial biomass (µg Cmic g-1 soil dry mass) and basal respiration (BR; µl O2 h-1 g-1 soil dry mass) as affected by plant species richness (SR) and CO2 concentrations. Dashed lines indicate ambient CO2 levels, solid lines elevated CO2 levels (+180 ppm). SR x CO2 for Cmic: p=0.007; SR x CO2 for BR: p=0.03). Data from August 2010. Means with SE. Redrawn after Eisenhauer et al. (2013).
Figure 4 from: Eisenhauer N (2018) Aboveground-belowground interactions drive the relationship between plant diversity and ecosystem function. Research Ideas and Outcomes 4: e23688. https://doi.org/10.3897/rio.4.e23688
Figure 4 - Conceptual figure showing how global change drivers like temperature increase and drought may increase plant diversity–ecosystem function relationships.
Figure 2 from: Eisenhauer N (2018) Aboveground-belowground interactions drive the relationship between plant diversity and ecosystem function. Research Ideas and Outcomes 4: e23688. https://doi.org/10.3897/rio.4.e23688
Figure 2 - Conceptual scheme of how aboveground–belowground interactions may influence the positive relationship between plant diversity and ecosystem functioning. The left part of the scheme illustrates how lower quantity and quality of plant inputs to the soil in species-poor plant communities (being low in resource use complementarity) may induce negative soil feedback effects. The right part of the scheme shows that higher quantity and quality of plant inputs in species-rich plant communities (being high in resource use complementarity) may cause the dominance of positive soil feedback effects. Mutualists will decrease (Wurst et al. 2008, Latz et al. 2012) and/or superimpose (Eisenhauer et al. 2012a) detrimental effects of antagonists on plants. The four proposed projects complement each other to explore the underlying mechanisms of this scheme across different experimental contexts.
Figure 1b from: Eisenhauer N (2018) Aboveground-belowground interactions drive the relationship between plant diversity and ecosystem function. Research Ideas and Outcomes 4: e23688. https://doi.org/10.3897/rio.4.e23688
Figure 1b - Importance of the duration of the experiment for its outcome. <br> Long-term plant diversity studies on soil biota are rare. Relationship between sampling time since the establishment of the biodiversity experiment, number of studies investigating soil biota and percentage of significant plant diversity effects on soil biota (Eisenhauer et al. 2012a). Size of the bubbles and respective numbers indicate percentage of significant plant diversity effects (regression between number of studies and time: R²=0.56, p=0.033, between time and significant plant diversity effects: R²=0.66, p=0.014, n=20 studies).
Supplementary material 1 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042
Detailed design of the Field Experiment
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