3,077 research outputs found

    Microbial impact on initial soil formation in arid and semiarid environments under simulated climate change

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    The microbiota is attributed to be important for initial soil formation under extreme climate conditions, but experimental evidence for its relevance is scarce. To fill this gap, we investigated the impact of in situ microbial communities and their interrelationship with biocrust and plants compared to abiotic controls on soil formation in initial arid and semiarid soils. Additionally, we assessed the response of bacterial communities to climate change. Topsoil and subsoil samples from arid and semiarid sites in the Chilean Coastal Cordillera were incubated for 16 weeks under diurnal temperature and moisture variations to simulate humid climate conditions as part of a climate change scenario. Our findings indicate that microorganism-plant interaction intensified aggregate formation and stabilized soil structure, facilitating initial soil formation. Interestingly, microorganisms alone or in conjunction with biocrust showed no discernible patterns compared to abiotic controls, potentially due to water-masking effects. Arid soils displayed reduced bacterial diversity and developed a new community structure dominated by Proteobacteria, Actinobacteriota, and Planctomycetota, while semiarid soils maintained a consistently dominant community of Acidobacteriota and Proteobacteria. This highlighted a sensitive and specialized bacterial community in arid soils, while semiarid soils exhibited a more complex and stable community. We conclude that microorganism-plant interaction has measurable impacts on initial soil formation in arid and semiarid regions on short time scales under climate change. Additionally, we propose that soil and climate legacies are decisive for the present soil microbial community structure and interactions, future soil development, and microbial responses

    Seasonal dynamics of the pelagic Sylt-Römö Bight food web

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    The living conditions for planktonic organisms in the Wadden Sea have dramatically changed during the past (Eriksson et al., 2010) and under progressing climate change these pelagic communities now are facing new threats. Warming winters induce shifts in blooming phenology (Greve et al., 2001) and production patterns of zooplankton (Martens & van Beusekom, 2008; Büttger et al., 2011), and these ecological changes may perturb the pelagic food at different moments of the year. This work investigated for the first time how the pelagic trophic network of the Sylt-Romo Bight, Northern Wadden Sea, changes across (1) spring, summer and autumn seasons and (2) between years which differed in terms of temperature during the preceding winter (2009: warm vs. 2010: cold). We therefore constructed six seasonal carbon flow networks that portrayed material exchanges among 16 trophic groups (bacteria, phyto- and zooplankton, C. harengus) and two non-living carbon pools (POC, DOC) and exchanges with adjacent systems (imports, exports). This approach used linear inverse modeling in combination with Markov Chain Monte Carlo sampling (LIM-MCMC), timeseries data (GPP, NPP, biomass) and literature information (diets, physiological rates) to quantify the carbon flows of the seasonal networks, accounting for uncertainties in carbon flow estimates linked to data constraints. We then applied ecological network analysis (ENA) to assess seasonal and inter-annual changes in the structure and functioning of the pelagic food web (i.e. activity, trophic transfer efficiency, connectance, recycling, flow organization) by means of network-derived whole system indices. Our analysis revealed a pelagic food web that is very inefficient at processing input energy (i.e. high imports and exports; low recycling; low trophic transfer), and this feature highlights its important function in supplying energy to benthic food webs (i.e. high export of POC, unconsumed primary and zooplankton production). Moreover, this system displays a high capacity of absorbing seasonal and inter-annual shifts in plankton biomass as it mostly maintained its structure and functioning during and across years (i.e. few seasonal and inter-annual differences in whole system indices). Finally, we observed a peculiar situation in autumn of 2009 when the food web shifted to a less active, less connected but more rigidly organized energy flow structure than compared to all other seasons. However, we found no strong support for presuming that this short term shift in energy flow structure had been triggered by the preceding warmer winter climate. We therefore conclude that the pelagic food web of SRB was insensitive to the climate of the preceding winter during 2009 and 2010, but more recent field data is urgently needed to test the generality of this conclusion

    Parameters of the arthropod–exotic legume plant interaction networks on the island of Santa Cruz and their respective p values.

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    SI: Sampling intensity; C: Connectance; IE: Interaction evenness; ISA: Interaction strength asymmetry; IR: Interaction richness; AR: Arthropod species richness; PR: Plant species richness. p values refer to the effect of each land use type and number of trophic levels on each of the parameters estimated by GLM, using sampling intensity as a covariate.</p

    Linking the impact of bacteria on phytoplankton growth with microbial community composition and co-occurrence patterns

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    The interactions between microalgae and bacteria have recently emerged as key control factors which might contribute to a better understanding on how phytoplankton communities assemble and respond to environmental disturbances. We analyzed partial 16S rRNA and 18S rRNA genes from a total of 42 antibiotic bioassays, where phytoplankton growth was assessed in the presence or absence of an active bacterial community. A significant negative impact of bacteria was observed in 18 bioassays, a significant positive impact was detected in 5 of the cases, and a non-detectable effect occurred in 19 bioassays. Thalasiossira spp., Chlorophytes, Vibrionaceae and Alteromonadales were relatively more abundant in the samples where a positive effect of bacteria was observed compared to those where a negative impact was observed. Phytoplankton diversity was lower when bacteria negatively affect their growth than when the effect was beneficial. The phytoplankton-bacteria co-occurrence subnetwork included many significant Chlorophyta-Alteromonadales and Bacillariophyceae-Alteromonadales positive associations. Phytoplankton-bacteria co-exclusions were not detected in the network, which contrasts with the negative effect of bacteria on phytoplankton growth frequently detected in the bioassays, suggesting strong competitive interactions. Overall, this study adds strong evidence supporting the key role of phytoplanktonbacteria interactions in the microbial communities.Agencia Estatal de Investigación | Ref. CTM2017-83362-RAgencia Estatal de Investigación | Ref. PID2019-110011RB-C33Xunta de Galicia | Ref. ED481A-2019/290Xunta de Galicia | Ref. ED481A-2018/288Universidade de Vigo/CISU

    Pollination Responses to Introduced Plants and an Elevation Gradient in Camas Dominated Wet Meadows

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    Global change is driving declines in insect biodiversity, with widespread consequences for ecosystem function. Climate change and invasive species are key global change factors, but the ways in which they alter pollination are poorly understood in many systems. Camas meadows occur in the southwestern-most areas of Canada, where they support high floral and pollinator diversity, yet we know little about the pollination ecology of these meadows, let alone how they are impacted by aspects of global change. My objectives in this thesis were to evaluate evidence that camas meadows are experiencing impacts related to climate change and plant invasions. I used a pollen limitation experiment conducted across an elevation gradient to evaluate whether variation in climate generates phenological asynchrony between camas and its pollinators, and used plant-pollinator network analysis to examine whether introduced plants were driving changes in pollination networks. I found that there was no evidence for phenological asynchrony, though camas reproduction was slightly limited by pollen at low elevations, while overall seed production declined as camas approached its elevational limit. Introduced species did not alter network structure, but when removed from networks they had come to dominate, networks were less able to resist further species loss. This suggests that if maintaining pollination is desired, invasive species management decisions should consider the risks associated with losing the floral resources they seek to control. My results describe a system which in its current state, appears robust to the aspects of global change examined (i.e., phenological disturbance and plant invasion) but may be sensitive to further disruption, particularly the removal of abundant introduced plants that pollinators have come to rely upon

    Adaptive foraging of pollinators fosters gradual tipping under resource competition and rapid environmental change

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    Plant and pollinator communities are vital for transnational food chains. Like many natural systems, they are affected by global change: rapidly deteriorating conditions threaten their numbers. Previous theoretical studies identified the potential for community-wide collapse above critical levels of environmental stressors—so-called bifurcation-induced tipping points. Fortunately, even as conditions deteriorate, individuals have some adaptive capacity, potentially increasing the boundary for a safe operating space where changes in ecological processes are reversible. Our study considers this adaptive capacity of pollinators to resource availability and identifies a new threat to disturbed pollinator communities. We model the adaptive foraging of pollinators in changing environments. Pollinator’s adaptive foraging alters the dynamical responses of species, to the advantage of some—typically generalists—and the disadvantage of others, with systematic non-linear and non-monotonic effects on the abundance of particular species. We show that, in addition to the extent of environmental stress, the pace of change of environmental stress can also lead to the early collapse of both adaptive and nonadaptive pollinator communities. Specifically, perturbed communities exhibit rate-induced tipping points at stress levels within the safe boundary defined for constant stressors. With adaptive foraging, tipping is a more asynchronous collapse of species compared to nonadaptive pollinator communities, meaning that not all pollinator species reach a tipping event simultaneously. These results suggest that it is essential to consider the adaptive capacity of pollinator communities for monitoring and conservation. Both the extent and the rate of stress change relative to the ability of communities to recover are critical environmental boundaries

    DataSheet_1_Spatial analysis of demersal food webs through integration of eDNA metabarcoding with fishing activities.docx

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    The evaluation of the status of marine communities, and especially the monitoring of those heavily exploited by fisheries, is a key, challenging task in marine sciences. Fishing activities are a major source of disruption to marine food webs, both directly, by selectively removing components at specific trophic levels (TL), and indirectly, by altering habitats and production cycles. Food web analysis can be very useful in the context of an Ecosystem Approach to Fisheries, but food web reconstructions demand large and expensive data sets, which are typically available only for a small fraction of marine ecosystems. Recently, new technologies have been developed to easily, quickly and cost-effectively collect environmental DNA (eDNA) during fishing activities. By generating large, multi-marker metabarcoding data from eDNA samples obtained from commercial trawlers, it is possible to produce exhaustive taxonomic inventories for the exploited ecosystems, which are suitable for food-web reconstructions. Here, we integrate and re-analyse the data of a recent study in which the α diversity was investigated using the eDNA opportunistically collected during fishing operations. Indeed, we collect highly resolved information on species feeding relationships to reconstruct the food webs at different sites in the Strait of Sicily (Mediterranean Sea) from eDNA and catch data. After observing that the trophic networks obtained from eDNA metabarcoding data are more consistent with the available knowledge, a set of food web indicators (species richness, number of links, direct connectance and generality) is computed and analysed to unravel differences in food webs structure through different areas (spatial variations). Species richness, number of links and generality (positively) and direct connectance (negatively) are correlated with increasing distance from the coast and fishing effort intensity. The combined effects of environmental gradients and fishing effort on food web structure at different study sites are then examined and modelled. Taken together, these findings indicate the suitability of eDNA metabarcoding to assist and food web analysis, obtain several food web-related ecological indicators, and tease out the effect of fishing intensity from the environmental gradients of marine ecosystems.</p

    Spatial analysis of demersal food webs through integration of eDNA metabarcoding with fishing activities

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    The evaluation of the status of marine communities, and especially the monitoring of those heavily exploited by fisheries, is a key, challenging task in marine sciences. Fishing activities are a major source of disruption to marine food webs, both directly, by selectively removing components at specific trophic levels (TL), and indirectly, by altering habitats and production cycles. Food web analysis can be very useful in the context of an Ecosystem Approach to Fisheries, but food web reconstructions demand large and expensive data sets, which are typically available only for a small fraction of marine ecosystems. Recently, new technologies have been developed to easily, quickly and cost-effectively collect environmental DNA (eDNA) during fishing activities. By generating large, multi-marker metabarcoding data from eDNA samples obtained from commercial trawlers, it is possible to produce exhaustive taxonomic inventories for the exploited ecosystems, which are suitable for food-web reconstructions. Here, we integrate and re-analyse the data of a recent study in which the α diversity was investigated using the eDNA opportunistically collected during fishing operations. Indeed, we collect highly resolved information on species feeding relationships to reconstruct the food webs at different sites in the Strait of Sicily (Mediterranean Sea) from eDNA and catch data. After observing that the trophic networks obtained from eDNA metabarcoding data are more consistent with the available knowledge, a set of food web indicators (species richness, number of links, direct connectance and generality) is computed and analysed to unravel differences in food webs structure through different areas (spatial variations). Species richness, number of links and generality (positively) and direct connectance (negatively) are correlated with increasing distance from the coast and fishing effort intensity. The combined effects of environmental gradients and fishing effort on food web structure at different study sites are then examined and modelled. Taken together, these findings indicate the suitability of eDNA metabarcoding to assist and food web analysis, obtain several food web-related ecological indicators, and tease out the effect of fishing intensity from the environmental gradients of marine ecosystems

    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

    The relationship between R&D subsidy and R&D cooperation in eco-innovative companies. An analysis taking a complementarity approach

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    We analyze whether eco-innovation has a positive or negative influence on the business performance of companies and, through the complementarity approach, whether the joint implementation of R&D subsidy and R&D cooperation increases or decreases the sum of their respective individual impacts on the business performance. If the joint implementation is substitutive, business performance will be lower than potentially possible, so granting R&D subsidies under the condition of establishing R&D cooperation would not be an adequate policy to promote eco-innovation. The analyses were performed using data from the Technological Innovation Panel (PITEC) of 2013 for Spanish manufacturing companies. Our findings indicate that an eco-innovation-oriented strategy positively affects the labor productivity of companies and that receiving public aid as a consequence of establishing R&D cooperation agreements has a lower effect on labor productivity (non-eco-innovative companies), or the same effect (eco-innovative companies), compared to the sum of the individual impacts of R&D cooperation and R&D subsidy. Consequently, in non-eco-innovative companies the use of subsidized R&D cooperation is inadvisable, while their use in eco-innovative companies is neutral
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