Multitrophic biodiversity enhances ecosystem functions, services and ecological intensification in agriculture

One central challenge for humanity is to mitigate and adapt to an ongoing climate and biodiversity crisis while providing resources to a growing human population. Ecological intensification (EI) aims to maximize crop productivity while minimizing impacts on the environment, especially by using biodiversity to improve ecosystem functions and services. Many EI measures are based on trophic interactions between organisms (e.g. pollination, biocontrol). Here, we investigate how research on multitrophic effects of biodiversity on ecosystem functioning could advance the application of EI measures in agriculture and forestry. We review previous studies and use qualitative analyses of the literature to test how important variables such as land-use parameters or habitat complexity affect multitrophic diversity, ecosystem functions and multitrophic biodiversity–ecosystem functioning relationships. We found that positive effects of biodiversity on ecosystem functions are prevalent in production systems, largely across ecosystem function dimensions, trophic levels, study methodologies and different ecosystem functions, however, with certain context dependencies. We also found strong impacts of land use and management on multitrophic biodiversity and ecosystem functions. We detected knowledge gaps in terms of data from underrepresented geographical areas, production systems, organism groups and functional diversity measurements. Additionally, we identified several aspects that require more attention in the future, such as trade-offs between multiple functions, temporal dynamics, effects of climate change, the spatial scale of the measures and their implementation. This information will be vital to ensure that agricultural and forest landscapes produce resources for humanity sustainably within the environmental limits of the planet

Fear of predation alters clone-specific performance in phloem-feeding prey

Fear of predation has been shown to affect prey fitness and behaviour, however, to date little is known about the underlying genetics of responses to predator-associated risk. In an effort to fill this gap we exposed four naïve clones of green peach aphid (Myzus persicae), maintained on the model crop Brassica oleracea, to different types of cues from aphid lion (Chrysoperla carnea). The respective predation risks, we termed Fear Factors, were either lethal (consumption by predator), or non-lethal (non-consumptive predator- associated cues: plant-tethered predator cadavers and homogenised shoot- sprayed or soil-infused blends of predator remains). Our results show that the non-lethal risk cues differentially impeded prey reproductive success that varied by clone, suggesting genotype-specific response to fear of predation. Furthermore, whether plants were perceived as being safe or risky influenced prey responses as avoidance behaviour in prey depended on clone type. Our findings highlight that intra-specific genetic variation underlies prey responses to consumptive and non-consumptive effects of predation. This allows selection to act on anti-predator responses to fear of predation that may ramify and influence higher trophic levels in model agroecosystems

Direct and indirect effects of land-use intensity on plant communities across elevation in semi-natural grasslands

Grassland biodiversity is vulnerable to land use change. How to best manage semi-natural grasslands for maintaining biodiversity is still unclear in many cases because land-use processes may depend on environmental conditions and the indirect effects of land-use on biodiversity mediated by altered abiotic and biotic factors are rarely considered. Here we evaluate the relative importance of the direct and indirect effects of grazing intensity on plant communities along an elevational gradient on a large topographic scale in the Eastern Carpathians in Ukraine. We sampled for two years 31 semi-natural grasslands exposed to cattle grazing. Within each grassland site we measured plant community properties such as the number of species, functional groups, and the proportion of species undesirable for grazing. In addition, we recorded cattle density (as a proxy for grazing intensity), soil properties (bare soil exposure, soil organic carbon, and soil pH) and densities of soil decomposers (earthworms and soil microorganisms). We used structural equation modelling to explore the direct and indirect effects of grazing intensity on plant communities along the elevation gradient. We found that cattle density decreased plant species and functional diversity but increased the proportion of undesirable species. Some of these effects were directly linked to grazing intensity (i.e., species richness), while others (i.e., functional diversity and proportion of undesirable species) were mediated via bare soil exposure. Although grazing intensity decreased with elevation, the effects of grazing on the plant community did not change along the elevation gradient. Generally, elevation had a strong positive direct effect on plant species richness as well as a negative indirect effect, mediated via altered soil acidity and decreased decomposer density. Our results indicate that plant diversity and composition are controlled by the complex interplay among grazing intensity and changing environmental conditions along an elevation gradient. Furthermore, we found lower soil pH, organic carbon and decomposer density with elevation, indicating that the effects of grazing on soil and related ecosystem functions and services in semi-natural grasslands may be more pronounced with elevation. This demonstrates that we need to account for environmental gradients when attempting to generalize effects of land-use intensity on biodiversity

Biodiversity increases multitrophic energy use efficiency, flow and storage in grasslands

The continuing loss of global biodiversity has raised questions about the risk that species extinctions pose for the functioning of natural ecosystems and the services that they provide for human wellbeing. There is consensus that, on single trophic levels, biodiversity sustains functions; however, to understand the full range of biodiversity effects, a holistic and multitrophic perspective is needed. Here, we apply methods from ecosystem ecology that quantify the structure and dynamics of the trophic network using ecosystem energetics to data from a large grassland biodiversity experiment. We show that higher plant diversity leads to more energy stored, greater energy flow and higher community-energy-use efficiency across the entire trophic network. These effects of biodiversity on energy dynamics were not restricted to only plants but were also expressed by other trophic groups and, to a similar degree, in aboveground and belowground parts of the ecosystem, even though plants are by far the dominating group in the system. The positive effects of biodiversity on one trophic level were not counteracted by the negative effects on adjacent levels. Trophic levels jointly increased the performance of the community, indicating ecosystem-wide multitrophic complementarity, which is potentially an important prerequisite for the provisioning of ecosystem services.</p

Biodiversity increases multitrophic energy use efficiency, flow and storage in grasslands

The continuing loss of global biodiversity has raised questions about the risk that species extinctions pose for the functioning of natural ecosystems and the services that they provide for human wellbeing. There is consensus that, on single trophic levels, biodiversity sustains functions; however, to understand the full range of biodiversity effects, a holistic and multitrophic perspective is needed. Here, we apply methods from ecosystem ecology that quantify the structure and dynamics of the trophic network using ecosystem energetics to data from a large grassland biodiversity experiment. We show that higher plant diversity leads to more energy stored, greater energy flow and higher community-energy-use efficiency across the entire trophic network. These effects of biodiversity on energy dynamics were not restricted to only plants but were also expressed by other trophic groups and, to a similar degree, in aboveground and belowground parts of the ecosystem, even though plants are by far the dominating group in the system. The positive effects of biodiversity on one trophic level were not counteracted by the negative effects on adjacent levels. Trophic levels jointly increased the performance of the community, indicating ecosystem-wide multitrophic complementarity, which is potentially an important prerequisite for the provisioning of ecosystem services

Multitrophic energy dynamics (energy-use efficiency, energy flow, and energy storage) in the Jena Experiment (Main Experiment)

This data set contains measures of energy-use efficiency, energy flow, and energy storage in units of dry biomass that quantify the multitrophic ecosystem functioning realized in grassland ecosystems of differing plant diversity. Given are both the measures integrated over whole ecosystems (total network measures) as well as the energy dynamics associated with individual ecosystem compartments including the entire biological community and detrital compartments across the above- and belowground parts of the ecosystem. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment, see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Study plots are grouped in four blocks in parallel to the river in order to account for any effect of a gradient in abiotic soil properties. Each block contains an equal number of plots of each plant species richness and plant functional group richness level. Plots were maintained in general by bi-annual weeding and mowing. Since 2010, plot size was reduced to 5.5 x 6 m and plots were weeded three times per year. Trophic-network models were constructed for 80 of the experimental plots, and represent the ecosystem energy budget in the currency of dry-mass (g m-2 for standing stocks and g m-2 d-1 for flows). All trophic networks have the same topology, but they differ in the estimated size of the standing stock biomass of individual compartments (g m-2) and flows among the compartments (g m-2 d-1). Each trophic network contains twelve ecosystem compartments representing distinct trophic groups of the above- and belowground parts of the ecosystem (i.e., plants, soil microbial community, and above- and belowground herbivores, carnivores, omnivores, decomposers, all represented by invertebrate macro- and mesofauna) and detrital pools (i.e., surface litter and soil organic matter). Vertebrates were not considered in our study due to limitations of data availability and because the impact of resident vertebrates in our experimental system is expected to be minimal. Larger grazing vertebrates were excluded by a fence around the field site, though there was some occasional grazing by voles. Compartments are connected by 41 flows. Flows (fluxes) constitute 30 internal flows within the system, namely feeding (herbivory, predation, decomposition), excretion, mortality, and mechanical transformation of surface litter due to bioturbation plus eleven 11 external flows, i.e. one input (flows entering the system, namely carbon uptake by plants) and ten output flows (flows leaving the system, namely respiration losses). The ecosystem inflow (a flow entering the system) and outflows (flows leaving the system) represent carbon uptake and respiration losses, respectively. In the case of consumer groups, the food consumed (compartment-wide input flow) is further split into excretion (not assimilated organic material that is returned to detrital pools in the form of fecesfaeces) and assimilated organic material, which is further split into respiration (energy lost out of the system to the environment) and biomass production, which is further consumed by higher trophic levels due to predation or returned to detrital pools in the form of mortality (natural mortality or prey residues). In case of detrital pools (i.e. surface litter and soil organic matter), the input flows are in the form of excretion and mortality from the biota compartments, and output flows are in the form of feeding by decomposers and soil microorganisms (i.e. decomposition). Surface litter and soil organic matter are connected by flows in the form of burrowing (mechanical transportation) of organic material from the surface to the soil by soil fauna. Organism immigration and emigration are not considered in our study due to limited data availability. Flows were quantified using resource processing rates (i.e. the feeding rates at which material is taken from a source) multiplied with the standing biomass of the respective source compartment. To approximate resource processing rates, different approaches were used: (i) experimental measurements (namely the aboveground decomposition, fauna burial activity (bioturbation), microbial respiration, and aboveground herbivory and predation rates); (ii) allometric equations scaled by individual body mass, environmental temperature and phylogenetic group (for the above- and belowground fauna respiration rates and plant respiration); (iii) assimilation rates scaled by diet type (for quantification of belowground fauna excretion and natural mortality); (iv) literature-based rates scaled by biomass of trophic groups (for microbial mortality); and (v) mass-balance assumptions (carbon uptake, plant and aboveground fauna mortality, belowground decomposition, belowground herbivory, and belowground predation). Mass-balance assumption means that the flows are calculated assuming that resource inputs into the compartment (i.e. feeding) balance the rate at which material is lost (i.e. the sum of through excretion, respiration, predation, and natural death). We used constrained nonlinear multivariable optimization to perturb the initial flow rates estimated from the various sources. We assigned confidence ratings for each flow rate, reflecting the quality of empirical data it is based on. We then used the 'fmincon' function from Matlab's optimization toolbox, which utilizes the standard Moore-Penrose pseudoinverse approach to achieve a balanced steady state ecological network model that best reflects the collected field data. Measured data used to parameterize the trophic network models were collected mostly in the year 2010. Network-wide measures that quantify proxies for different aspects of multitrophic ecosystem functioning were calculated for each experimental plot using the 'enaR' package in R. In particular, total energy flow was measured as the sum of all flows through each ecosystem compartment. Flow uniformity was calculated as the ratio of the mean of summed flows through each individual ecosystem compartment divided by the standard deviation of these means. Total-network standing biomass was determined as the sum of standing biomass across all ecosystem compartments. Community maintenance costs were calculated as the ratio of community-wide respiration related to community-wide biomass