11,054 research outputs found

    The coevolution of foraging effort and species abundances under increasing driver of decline <i>d</i><sub><i>A</i></sub>.

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    The top row shows the evolution of pollinator and plant abundances under linearly increasing driver of decline dA with rate λ = 0.05, starting from a low abundance condition of Sinit = 0.1, with adaptation strength of ν = 0.7 and resource congestion q = 0.2. Each line style and color combination represents a single pollinator species in all graphs (except the plant abundance graph, top right). For example, there is one pollinator species with degree 3. Since the degree is 3, there are three solid blue lines (one for each connection to a plant species). Another example is the two pollinator species with degree 4, thus showing eight lines (four solid lines for one species and four dashed lines for another species). Since the values of the foraging effort α for each individual species add up to 1, the evolution of the foraging effort α is not shown for pollinators with degree one since they have a constant α = 1 to their single connected plant species. Pollinators with high degree rapidly become the most abundant. Furthermore, the foraging effort α drastically changes—especially around 10 time units when most species reach their peak abundance. The two pollinator species with degree 9 survive the longest and also have one plant species in which they invest most of their foraging effort after 10 time units. Other parameters in Table A in S1 Text.</p

    Species persistence collapses for high rates of environmental change within environmental ranges that are otherwise presumed safe.

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    (A) Three scenarios are represented for communities without adaptive foraging, perturbed to start with a low initial species abundance (out of equilibrium). (Inset) Stress in communities increases over time as the driver of decline increases at different rates, λ, up to a maximum value, . The black line represents the point of collapse above which a fixed value of the driver of decline leads to the collapse of all communities and below which some communities are sustained. The maximum value of the driver of decline in each simulation is denoted by the fraction θ of the point of collapse . (A) Dotted orange line represents an increase in the driver of decline up to 90% of the critical value, , dot-dashed green line an increase up to 50%, and dashed pink an increase up to 20%. In this panel, species persistence is calculated as the fraction of pollinator species alive relative to the number of species alive at the lowest rate of change measured (λmin = 10−4). (B) The persistence of species decreases as a function of the maximum value of the driver of decline, represented as a fraction θ of the point of collapse, for a fast rate of change (λ = 1). Communities without adaptive foraging see a critical transition in species persistence when the driver of decline increases to a value close to, but lower than, the point of collapse at a fast enough rate. In this panel, species persistence is calculated as the fraction of pollinator species alive relative to the number of species alive at θ = 0 (no external stressor). Initial species abundance Sinit = 0.1 for all simulations. The results are averaged over 100 feasible networks, for which all 15 plant and 35 pollinator species survive under no stress, with the bands representing the first to third quartile ranges. Other parameters in Table A in S1 Text.</p

    Adaptability and resource congestion affect hysteretic patterns and the viability of plant-pollinator networks.

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    For adaptive pollinators, intermediate levels of resource congestion increase the overall persistence of ecological networks. The point of collapse and recovery of pollinator species increases as a function of resource congestion q without (A) and with adaptive foraging (B). For low resource congestion, the system possesses bistable states which disappear after a critical value of the resource congestion. Resource congestion also affects the feasibility of the networks—networks for which all 15 plant and 35 pollinator species survive under no stress. An intermediate level of resource congestion is required for the adaptive model to produce feasible networks. The orange arrows indicate the resource congestion strength q chosen for the simulation of Figs 2 and 3. These values were chosen such that the systems possess bistable states—as observed in the non-overlapping points of collapse and recovery—and have a high fraction of feasibility. For low resource congestion, adaptability increases the range of drivers of decline at which pollinator communities do not collapse, increasing resilience in the Holling sense [41]. (A) ν = 1 and (B) ν = 0.7. The results are averaged over 100 networks per value of resource congestion q with the error bars showing the standard deviation. Other parameters in Table A in S1 Text.</p

    The effect of silicon on the antioxidant system of tomato seedlings exposed to individual and combined nitrogen and water deficit

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    Exploring sustainable strategies for improving crop water and nitrogen use efficiency is essential. Silicon (Si) has been reported as a beneficial metalloid for plants since it alleviates several abiotic stresses (including drought) by triggering the plants´ antioxidant system. However, its role in mitigating the negative impact of nitrogen (N) deficit alone or when combined with water (W) deficit is not well studied. This study applied 0 or 2 mM of Na₂SiO to 3-week-old tomato cv. Micro-Tom seedlings that were grown under the following conditions: control (CTR; 100%N+100% Field Capacity), N deficit (N; 50% N + 100% Field Capacity), water deficit (W; 100% N + 50% Field Capacity) or combined stress (N+W; 50% N + 50% Field Capacity. The Si effect on tomato plant growth depended on the type of stress. Si could only alleviate stress caused by N+W deficit resulting in a higher root dry weight (by 28%), total dry weight (by 23%) and root length (by 37%). Alongside this, there was an increase in the antioxidant (AOX) system activity with the root activity of the studied AOX enzymes APX and CAT being enhanced by 48% and by 263%, respectively. Si application also enhanced AOX enzyme activity when tomato plants were subjected to individual deficits but to a lesser extent. In conclusion, Si-treated tomato plants could efficiently modulate their AOX networks in a situation of combined N and water limitation, thus mitigating some of the adverse effects of this combined stress.info:eu-repo/semantics/acceptedVersio

    International care programs for Pediatric Post-COVID Condition (Long COVID) and the way forward

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    Background: Pediatric Post-COVID-Condition (PPCC) clinics treat children despite limited scientific substantiation. By exploring real-life management of children diagnosed with PPCC, the International Post-COVID-Condition in Children Collaboration (IP4C) aimed to provide guidance for future PPCC care. Methods: We performed a cross-sectional international, multicenter study on used PPCC definitions; the organization of PPCC care programs and patients characteristics. We compared aggregated data from PPCC cohorts and identified priorities to improve PPCC care. Results: Ten PPCC care programs and six COVID-19 follow-up research cohorts participated. Aggregated data from 584 PPCC patients was analyzed. The most common symptoms included fatigue (71%), headache (55%), concentration difficulties (53%), and brain fog (48%). Severe limitations in daily life were reported in 31% of patients. Most PPCC care programs organized in-person visits with multidisciplinary teams. Diagnostic testing for respiratory and cardiac morbidity was most frequently performed and seldom abnormal. Treatment was often limited to physical therapy and psychological support. Conclusions: We found substantial heterogeneity in both the diagnostics and management of PPCC, possibly explained by scarce scientific evidence and lack of standardized care. We present a list of components which future guidelines should address, and outline priorities concerning PPCC care pathways, research and international collaboration

    Holocene paleo-redox conditions in a microbial dolomitic lake using benthic foraminifera as bioindicators

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    Brejo do Espinho coastal lake (LBE) is one of the few places in the world where dolomite [CaMg(CO3)2] is precipitating in the modern environment under microbially induced processes and low oxygen conditions. We use pore morphometry of the foraminifera Ammonia cf. A. veneta to evaluate paleo-O2 dynamics during the dolomitic depositional phase that took place at LBE in the late Holocene. Foraminiferal community structure was also investigated, and results were compared to bulk isotopic composition of carbonates, total organic carbon (TOC), and X-ray Diffraction of sediments (XRD). The correlation matrix (Spearman method) showed that Ammonia test pores morphometric parameters displayed significant correlations with overall biotic and geochemical data, with pore area presenting a relatively higher association. Ammonia test pores were primarily controlled by the degradation of organic matter (Pore area-TOC, r = −0.84), and foraminifera density appeared to be influenced by oxygen changes, with a higher abundance in the highest porosity intervals (Pore area-N, r = 0.82), indicating a direct effect of oxygen penetration on species dominance. These data also reveal a tolerant behavior of the low-O2 bioindicator species Quinqueloculina laevigata and A. veneta. Understanding microbe-mineral interactions is critical for interpreting paleo records, and our data provide strong support for coupling assemblage and pores analysis as paleo-O2 bioindicators for paleo-redox coastal settings.</p

    Text with supporting information.

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    The text with supporting information contains additional computational experiments and algorithms. It is divided into five sections. Section A. Simulation set-up. Includes: Table A. Default model parameters. The default parameter values and ranges used in all simulations, unless otherwise specified. AF = Adaptive Foraging, ∼ U(⋅) = drawn from a uniform distribution at the beginning of each simulation. Section B. Network generation. Describes the network generation algorithm and contains: Fig A. Adjacency matrix of a nested network and forbidden links Adjacency matrix of a pollinator network on the left with the corresponding forbidden links matrix on the right. Black squares denote the presence of a link. The connectance is 0.15 and the fraction of forbidden links is 0.3. There is a clear difference visible between generalist species and specialist species, in the sense that there are a few species with high connectivity and many with low connectivity. Section C. Dependence of hysteresis on resource congestion and adaptation. Contains various additional computation experiments. Fig B. Hysteresis for increasing resource congestion q for the non-adaptive model. Equilibrium abundance of pollinator species as a function of the drivers of decline dA for increasing resource congestion q for the non-adaptive model. The blue lines show the equilibrium trajectory for increasing dA and the orange lines show the equilibrium trajectory for decreasing dA. See Table A for the parameters used. Fig C. Hysteresis for increasing resource congestion q for the adaptive model with ν = 0.8. Equilibrium abundance of pollinator species as a function of the drivers of decline dA for increasing resource congestion q for the adaptive model with ν = 0.8. The blue lines show the equilibrium trajectory for increasing dA and the orange lines show the equilibrium trajectory for decreasing dA. See Table A for the parameters used. Fig D. Hysteresis for increasing resource congestion q for the adaptive model with ν = 0.7. Equilibrium abundance of pollinator species as a function of the drivers of decline dA for increasing resource congestion q for the adaptive model with ν = 0.7. The blue lines show the equilibrium trajectory for increasing dA and the orange lines show the equilibrium trajectory for decreasing dA. See Table A for the parameters used. Fig E. Hysteresis for increasing resource congestion q for the adaptive model with ν = 0.6. Equilibrium abundance of pollinator species as a function of the drivers of decline dA for increasing resource congestion q for the adaptive model with ν = 0.6. The blue lines show the equilibrium trajectory for increasing dA and the orange lines show the equilibrium trajectory for decreasing dA. See Table A for the parameters used. Section D. Distribution of pollinator persistence. Describes the distribution of pollinator persisitence in different computational experiments. Fig F. The full distribution of relative pollinator abundance for three different rates of change λ accompanying Fig 2A in the paper (no adaptive foraging).θ is the fraction of the point of collapse dA at which point the relative pollinator persistence is measured. The distributions are bimodal around 0 and 1 which indicates that there is an abrupt collapse of networks at increasing rates of change. See Table A for the parameters used. Fig G. The full distribution of relative pollinator abundance for three different rates of change λ accompanying Fig 2A in the paper (with adaptive foraging).θ is the fraction of the point of collapse dA at which point the relative pollinator persistence is measured. The distributions are mainly bimodal around 0 and 1. However, some networks have a persistence between 0 and 1, indicating partial collapse due to the rate of change. Furthermore, there are a few networks with pollinator persistence significantly above 1, indicating nonlinear effects where sometimes individual networks can profit from higher rates of change. See Table A for the parameters used. Section E. Sensitivity analysis. Contains: Table B. Parameters for the sensitivity analysis on the feasibility of networks. Parameters and their value ranges used for the sensitivity analysis on the feasibility of networks, and plant and pollinator abundances. The fixed parameters can be found in Table A. AF = Adaptive Foraging. Fig H. Sensitivity analysis of the number of plant species alive. Sobol sensitivity analysis of the number of plant species alive depending on five parameters: resource congestion q, nestedness N, connectance D, adaptation strength ν, and migration rate μ. The sample size per parameter was 512. The adaptation strength ν had the strongest effect on the variance of the outcome of the model. Fig I. Sensitivity analysis of the number of pollinator species alive. Sobol sensitivity analysis of the number of pollinator species alive depending on five parameters: resource congestion q, nestedness N, connectance D, adaptation strength ν, and migration rate μ. The sample size per parameter was 512. The adaptation strength ν had the strongest effect on the variance of the number of pollinators alive. The migration rate μ only has a marginal effect. Fig J. Sensitivity analysis of the total number of species alive. Sobol sensitivity analysis of the total number of species alive depending on five parameters: resource congestion q, nestedness N, connectance D, adaptation strength ν, and migration rate μ. The sample size per parameter was 512. The adaptation strength ν had the strongest effect on the variance of the outcome of the model. Fig K. Sensitivity analysis of the abundance of plant species. Sobol sensitivity analysis of the average plant abundance depending on five parameters: resource congestion q, nestedness N, connectance D, adaptation strength ν, and migration rate μ. The sample size per parameter was 512. Fig L. Sensitivity analysis of the abundance of pollinator species. Sobol sensitivity analysis of the average pollinator abundance depending on five parameters: resource congestion q, nestedness N, connectance D, adaptation strength ν, and migration rate μ. The sample size per parameter was 512. Table C. Parameters for the sensitivity analysis on the critical driver of decline of collapse . Parameters and their value ranges used for the sensitivity analysis on the critical driver of decline of collapse . The fixed parameters can be found in Table A. AF = Adaptive Foraging, dA = driver of decline. Fig M. Sensitivity analysis on the driver of decline dA. Sobol sensitivity analysis on the value of driver of decline at which all pollinator are extinct , depending on six parameters: resource congestion q, nestedness N, connectance D, adaptation strength ν, initial abundance per species Sinit, and migration rate μ. The sample size per parameter was 512. (PDF)</p

    Pollinator communities with adaptive foraging still collapse at high rates of change but less abruptly in the extent of environmental change.

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    Figure equivalent to Fig 2, but considering adaptation and resource congestion. Adaptive communities respond to an increasing driver of decline by reweighing their connections. (A) Rate-induced transitions are still present, with some communities exhibiting rate-dependent tipping at 50% of the point of collapse. Non-monotonicity is within the error range, thus, non-significant for the number of simulations. (B) Overall, pollinator persistence is more sensitive to rates of change in a larger domain of changes in the driver of decline, θ, than for communities without adaptive foraging. Some particular networks see an increase in persistence, especially for small changes and low rates of change, leading to distinct relative persistence levels above 1. For all simulations, initial species abundance of Sinit = 0.1. Adaptation strength of ν = 0.7 and resource congestion q = 0.2. The results are averaged over 100 feasible networks, for which all 15 plant and 35 pollinator species survive under no stress, with the bands representing the first to third quartile ranges. Other parameters in Table A in S1 Text.</p
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