Sabrina Gaba

Host–parasite ‘Red Queen’ dynamics archived in pond sediment

Antagonistic interactions between hosts and parasites are a key structuring force in natural populations, driving coevolution. However, direct empirical evidence of long-term host–parasite coevolution, in particular ‘Red Queen’ dynamics—in which antagonistic biotic interactions such as host–parasite interactions can lead to reciprocal evolutionary dynamics—is rare, and current data, although consistent with theories of antagonistic coevolution, do not reveal the temporal dynamics of the process. Dormant stages of both the water flea Daphnia and its microparasites are conserved in lake sediments, providing an archive of past gene pools. Here we use this fact to reconstruct rapid coevolutionary dynamics in a natural setting and show that the parasite rapidly adapts to its host over a period of only a few years. A coevolutionary model based on negative frequency-dependent selection, and designed to mimic essential aspects of our host–parasite system, corroborated these experimental results. In line with the idea of continuing host–parasite coevolution, temporal variation in parasite infectivity changed little over time. In contrast, from the moment the parasite was first found in the sediments, we observed a steady increase in virulence over time, associated with higher fitness of the parasite.

Time-shift experiments as a tool to study antagonistic coevolution

Although understanding natural selection in antagonistic host-parasite interactions has been a challenge for many years, direct evidence for the coevolutionary process is still scarce, particularly in relation to changes in antagonist populations over time. The underlying processes of coevolution thus remain difficult to characterise. Time-shift experiments can be used to test the performance of an antagonist population from a moment in time against the other from the same and different moments in time, revealing reciprocal adaptation in host-parasite relationships. Here we discuss how time-shift experiments together with modelling can shed new insights on the underlying processes of antagonistic coevolution.

Multiple cropping systems as drivers for providing multiple ecosystem services: from concepts to design

Provisioning services, such as the production of food, feed, and fiber, have always been the main focus of agriculture. Since the 1950s, intensive cropping systems based on the cultivation of a single crop or a single cultivar, in simplified rotations or monocultures, and relying on extensive use of agrochemical inputs have been preferred to more diverse, self-sustaining cropping systems, regardless of the environmental consequences. However, there is increasing evidence that such intensive agroecosystems have led to a decline in biodiversity as well as threatening the environment and have damaged a number of ecosystem services such as the biogeochemical nutrient cycles and the regulation of climate and water quality. Consequently, the current challenge facing agriculture is to ensure the future of food production while reducing the use of inputs and limiting environmental impacts and the loss of biodiversity. Here, we review examples of multiple cropping systems that aim to use biotic interactions to reduce chemical inputs and provide more ecosystem services than just provisioning. Our main findings are the identification of underlying ecological processes and management strategies related to the provision of pairs of ecosystem services namely food production and a regulation service. We also found gaps between ecological knowledge and the constraints of agricultural practices in taking account of the interactions and possible trade-offs between multiple ecosystem services as well as socioeconomic constraints. We present guidelines for the design of multiple cropping systems combining ecological, agricultural, and genetic concepts and approaches.

Experimental and modeling approaches to evaluate different aspects of the efficacy of Targeted Selective Treatment of anthelmintics against sheep parasite nematodes.

Maintaining a refugia i.e. a proportion of the population that is not exposed to selection by treatments has been suggested as an alternative to mass treatment in the management of nematode parasites of sheep. Three refugia have been identified: nematodes in untreated hosts, encysted larvae and free-living stages on pastures. Here we tested whether Targeted Selective Treatments (TST) could be relevant in controlling nematode infections and delaying anthelmintic resistance selection. We first conducted a one grazing season experiment to compare all flock treatment (the whole flock was treated monthly) and TST based on monthly excretion eggs and daily weight gain. Nematode burden was higher in TST group, but anthelmintic susceptibility of nematodes was preserved. We then used an individual-based model to evaluate the sustainability of TST on a longer period. The simulation approach indicated that TST20% and TST30% of the flock were efficient both at maintaining resistance at a low level and controlling nematode parasite infections on a mid-term period (i.e. simulation of five grazing seasons). However for an efficient TST, these percentages of treated hosts should be adapted to flock size since the number of nematode parasites exposed to selection increases in large flocks. Our results also showed a high dependence on the timing of treatment i.e. on the size of the refugia constituted by the free-living stages on the pasture.

The early drug selection of nematodes to anthelmintics: stochastic transmission and population in refuge.

We have developed an individual-based model to reflect the complexity of the early phase of drug resistance selection in a nematode/sheep model. The infection process consists of the stochastic ingestion of infective larvae spatially aggregated in clumps. Each clump corresponds to infective larvae, which are the offspring of the mature nematodes from a given sheep. We studied the dynamics of the parasitic population and the frequency of the recessive resistance alleles during selection by anthelmintic treatments. The interaction between genetic and demographic processes illustrated the trade-off between the control of the infection and the delay of resistance selection. We confirmed the importance of the number of treatments and their timing. The same treatment frequency may result in different outcomes on resistance selection in relation to the size of the refuge (infective larvae on pasture). Treatment applied during the summer (when the mortality of infective larvae on pasture was high), may lead to a rapid selection of drug resistance and a lack of control of sheep and pasture contamination. We showed that higher stocking rates were also a force in promoting the resistance allele selection.

Modelling macroparasite aggregation using a nematode-sheep system: the Weibull distribution as an alternative to the negative binomial distribution?

Macroparasites are almost always aggregated across their host populations, hence the Negative Binomial Distribution (NBD) with its exponent parameter k is widely used for modelling, quantifying or analysing parasite distributions. However, many studies have pointed out some drawbacks in the use of the NBD, with respect to the sensitivity of k to the mean number of parasites per host or the under-representation of the heavily infected hosts in the estimate of k. In this study, we compare the fit of the NBD with 4 other widely used distributions on observed parasitic gastrointestinal nematode distributions in their sheep host populations (11 datasets). Distributions were fitted to observed data using maximum likelihood estimator and the best fits were selected using the Akaikes Information Criterion (AIC). A simulation study was also conducted in order to assess the possible bias in parameter estimations especially in the case of small sample sizes. We found that the NBD is seldom the best fit for gastrointestinal nematode distributions. The Weibull distribution was clearly more appropriate over a very wide range of degrees of aggregation, mainly because it was more flexible in fitting the heavily infected hosts. Moreover, the Weibull distribution estimates are less sensitive to sample size. Thus, when possible, we suggest to carefully check on observed data if the NBD is appropriate before conducting any further analysis on parasite distributions.

Modelling interaction networks for enhanced ecosystem services in agroecosystems

The development of new methods and approaches for ensuring the sustainability of agriculture and ecosystem services is an important challenge that ecologists, agronomists, and theoreticians must address together. Enhancement of ecosystem services needs to be addressed at different scales and should include the interaction between farmland biodiversity and stakeholders (farmers, managers, policy makers, etc.) to optimize service delivery. Predictions require an understanding of the interactions between numerous management options and components of biodiversity. Here, we argue that interaction networks on a broad sense (from food webs to landscapes networks in which nodes could be species, trophic groups, fields or farms) can help address this high level of complexity. We examine how tools from mathematics and artificial intelligence, developed for network modelling and reasoning, could be useful for assessing and enhancing ecosystems services. In doing this we highlight the gaps that currently exist between our questions about ecosystem service provision and our ability to answer them with current modelling approaches. We illustrate the use of these tools with three case studies related to ‘pest regulation services’. These include food web approaches to assess animal pest regulation services and decisional models to address management strategies for diseases and weeds. Finally, we describe how different types of network models might operate at different scales of management. The future challenge for agroecologists will be to produce models of interactions and emergent ecosystem services, which are sufficiently quantified and validated. We suggest that network ecology is a nascent research topic that is developing a strong and unified empirical and theoretical foundation, which could serve as the central paradigm for a sustainable, intensive agriculture in the future.

Antagonistic parent-offspring co-adaptation

Background In species across taxa, offspring have means to influence parental investment (PI). PI thus evolves as an interacting phenotype and indirect genetic effects may strongly affect the co-evolutionary dynamics of offspring and parental behaviors. Evolutionary theory focused on explaining how exaggerated offspring solicitation can be understood as resolution of parent-offspring conflict, but the evolutionary origin and diversification of different forms of family interactions remains unclear. Methodology/Principal Findings In contrast to previous theory that largely uses a static approach to predict how “offspring individuals” and “parental individuals” should interact given conflict over PI, we present a dynamic theoretical framework of antagonistic selection on the PI individuals obtain/take as offspring and the PI they provide as parents to maximize individual lifetime reproductive success; we analyze a deterministic and a stochastic version of this dynamic framework. We show that a zone for equivalent co-adaptation outcomes exists in which stable levels of PI can evolve and be maintained despite fast strategy transitions and ongoing co-evolutionary dynamics. Under antagonistic co-adaptation, cost-free solicitation can evolve as an adaptation to emerging preferences in parents. Conclusions/Significance We show that antagonistic selection across the offspring and parental life-stage of individuals favors co-adapted offspring and parental behavior within a zone of equivalent outcomes. This antagonistic parent-offspring co-adaptation does not require solicitation to be costly, allows for rapid divergence and evolutionary novelty and potentially explains the origin and diversification of the observed provisioning forms in family life.

Organic fields sustain weed metacommunity dynamics in farmland landscapes

Agro-ecosystems constitute essential habitat for many organisms. Agricultural intensification, however, has caused a strong decline of farmland biodiversity. Organic farming (OF) is often presented as a more biodiversity-friendly practice, but the generality of the beneficial effects of OF is debated as the effects appear often species- and context-dependent, and current research has highlighted the need to quantify the relative effects of local- and landscape-scale management on farmland biodiversity. Yet very few studies have investigated the landscape-level effects of OF; that is to say, how the biodiversity of a field is affected by the presence or density of organically farmed fields in the surrounding landscape. We addressed this issue using the metacommunity framework, with weed species richness in winter wheat within an intensively farmed landscape in France as model system. Controlling for the effects of local and landscape structure, we showed that OF leads to higher local weed diversity and that the presence of OF in the landscape is associated with higher local weed biodiversity also for conventionally farmed fields, and may reach a similar biodiversity level to organic fields in field margins. Based on these results, we derive indications for improving the sustainable management of farming systems.

Managing biotic interactions for ecological intensification of agroecosystems

Agriculture faces the challenge of increasing food production while simultaneously reducing the use of inputs and delivering other ecosystem services. Ecological intensification of agriculture is a paradigm shift, which has recently been proposed to meet such challenges through the manipulation of biotic interactions. While this approach opens up new possibilities, there are many constraints related to the complexity of agroecosystems that make it difficult to implement. Future advances, which are essential to guide agricultural policy, require an eco-evolutionary framework to ensure that ecological intensification is beneficial in the long term.