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The domestic dog is one of our most popular companions and longest relationships, occupying different roles, from pet to working guide dog for the blind. As dogs age different behavioural issues occur and in some cases dogs may be relinquished or removed from their working service. Here we analyse a dataset on working guide dogs that were removed from their service between 1994 and 2013. We use the withdrawal reasons as a proxy for the manifestation of undesirable behaviour. More than 7,500 dogs were in the dataset used, 83% of which were retired (due to old age) and 17% were withdrawn for behavioural issues. We found that the main reasons for behaviour withdrawal were environmental anxiety, training and fear/aggression. Breed and sex had an effect on the odds of dogs being withdrawn under the different reasons. The age at withdrawal for the different withdrawal reasons suggested that dogs were more likely to develop fear/aggression related issues early on, whilst issues related to training could develop at almost any age. We found no evidence for heterosis effecting behaviour. We believe that this work is relevant to the pet dog population and had implications for understanding ageing and genetic influences on behaviour.

Methods to predict ecosystem responses to changing environmental conditions, including options for mitigation and management, are vital for scientists and policy makers. We propose that modelling ecosystems by grouping species into trophic-functional types has great utility, because it is potentially generic and applicable to any ecosystem. Here we describe in details a trophic-functional ecosystem model of arable agriculture (the TrophicLINK model), constructed using an individual-based model, and present some supporting sensitivity tests of the model performing according to our expectations for the arable ecosystem. We vary parameters for particular plant and invertebrate trophic-functional types and we show that the model is sensitive to the values chosen. Though this trophic-functional method for modelling ecosystems will be subject to further sensitivity tests and empirical validation, we argue that the modelling structure is fit for investigating the impact of changes in agro-ecosystems.

Integrin signaling is a major pathway of cell adhesion to extracellular matrices that regulates many physiological cell behaviors such as cell proliferation, migration or differentiation and is implied in pathologies such as tumor invasion. In this paper, we focused on the molecular system formed by the two kinases FAK (focal adhesion kinase) and Src, which undergo auto- and co-activation during early steps of integrin signaling. The system is modelled using classical kinetic equations and yields a set of three nonlinear ordinary differential equations describing the dynamics of the different phosphorylation forms of FAK. Analytical and numerical analysis of these equations show that this system may in certain cases amplify incoming signals from the integrins. A quantitative condition is obtained, which indicates that the total FAK charge in the system acts as a critical mass that must be exceeded for amplification to be effective. Furthermore, we show that when FAK activity is lower than Src activity, spontaneous oscillations of FAK phosphorylation forms may appear. The oscillatory behavior is studied using bifurcation and stability diagrams. We finally discuss the significance of this behavior with respect to recent experimental results evidencing FAK dynamics.

International audience; Insecticide-treated nets and indoor residual spray programs for malaria control are entirely dependent on pyrethroid insecticides. The ubiquitous exposure of Anopheles mosquitoes to this chemistry has selected for resistance in a number of populations. This threatens the sustainability of our most effective interventions but no operationally practicable way of resolving the problem currently exists. One innovative solution involves the co-application of a powerful chemosterilant (pyriproxyfen or PPF) to bed nets that are usually treated only with pyrethroids. Resistant mosquitoes that are unaffected by the pyrethroid component of a PPF/pyrethroid co-treatment remain vulnerable to PPF. There is a differential impact of PPF on pyrethroid-resistant and susceptible mosquitoes that is modulated by the mosquito's behavioural response at co-treated surfaces. This imposes a specific fitness cost on pyrethroid-resistant phenotypes and can reverse selection. The concept is demonstrated using a mathematical model.

International audience; Ecosystems are increasingly manipulated for agricultural and conservation goals. Ecosystem functions need to be sustained socially and ecologically. New frameworks must be built to simulate agrosystems based on ecological processes instead of external chemicals. Food web structures of agrosystems highly influence their agronomical performance and stability. Although it has been observed that living communities are ruling the performance of agroecosystems, these living communities are generally ignored by agronomists who focused mainly on abiotic factors. Indeed, agronomists usually focus on the soil–plant–atmosphere continuum. Now, ecological modellers can link food web models with soil–plant models to create innovative frameworks. Here, we advocate that food webs must be included in simulations of production and in studies of emerging properties. We emphasize the role of trophic chains in the regulation of pests. Emerging properties include aboveground and belowground interactions, pest control, and positive feedbacks on soil properties. We propose a conceptual structure for this framework and discuss how the structure of linked food web/cropping system models can account for the specific properties of agroecosystems. The proposed structure includes a process-based approach to link food webs with crop models. Such comprehensive models address the issue of trade-offs between ecosystem services, including regulation of crop pests by the ecosystem community, nutrient cycling, and crop production.

Chapter 7; 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.

International audience; Networks of trophic links (food webs) are used to describe and understand mechanistic routes for translocation of energy (biomass) between species. However, a relatively low proportion of ecosystems have been studied using food web approaches due to difficulties in making observations on large numbers of species. In this paper we demonstrate that Machine Learning of food webs, using a logic-based approach called A/ILP, can generate plausible and testable food webs from field sample data. Our example data come from a national-scale Vortis suction sampling of invertebrates from arable fields in Great Britain. We found that 45 invertebrate species or taxa, representing approximately 25% of the sample and about 74% of the invertebrate individuals included in the learning, were hypothesized to be linked. As might be expected, detritivore Collembola were consistently the most important prey. Generalist and omnivorous carabid beetles were hypothesized to be the dominant predators of the system. We were, however, surprised by the importance of carabid larvae suggested by the machine learning as predators of a wide variety of prey. High probability links were hypothesized for widespread, potentially destabilizing, intra-guild predation; predictions that could be experimentally tested. Many of the high probability links in the model have already been observed or suggested for this system, supporting our contention that A/ILP learning can produce plausible food webs from sample data, independent of our preconceptions about "who eats whom." Well-characterised links in the literature correspond with links ascribed with high probability through A/ILP. We believe that this very general Machine Learning approach has great power and could be used to extend and test our current theories of agricultural ecosystem dynamics and function. In particular, we believe it could be used to support the development of a wider theory of ecosystem responses to environmental change.

Predicting ecosystem effects is of crucial importance in a world at threat from natural and human-mediated change. Here we propose an ecologically defensible representation of an ecosystem that facilitates predictive modelling. The representation has its roots in the early trophic and energetic theory of ecosystem dynamics and more recent functional ecology and network theory. Using the arable ecosystem of the UK as an example, we show that the representation allows simplification from the many interacting plant and invertebrate species, typically present in arable fields, to a more tractable number of trophic-functional types. Our compound hypothesis is that “trophic-functional types of plants and invertebrates can be used to explain the structure, diversity and dynamics of arable ecosystems”. The trophic-functional types act as containers for individuals, within an individual-based model, sharing similar trophic behaviour and traits of biomass transformation. Biomass, or energy, flows between the types and this allows the key ecological properties of individual abundance and body mass, at each trophic height, to be followed through simulations. Our preliminary simulation results suggest that the model shows great promise. The simulation output for simple ecosystems, populated with realistic parameter values, is consistent with current laboratory observations and provides exciting indications that it could reproduce field scale phenomena. The model also produces output that links the individual, population and community scales, and may be analysed and tested using community, network (food web) and population dynamic theory. We show that we can include management effects, as perturbations to parameter values, for modelling the effects of change and indicating management responses to change. This model will require robust analysis, testing and validation, and we discuss how we will achieve this in the future.

The use of mathematical and simulation models is widespread in ecology, and individual-based models (IBMs) have proved valuable for exploring individually-explicit interactions and behaviour. The success of a model will depend upon its design and the different assumptions made during construction. In particular, methods implemented in the model to deal with interactions between objects are of fundamental importance for producing appropriate results. Asynchronous and synchronous scheduling are two methods for updating object characteristics during interaction. The consequence of these updating methods has been investigated for cellular automata, but not for IBMs. Here, we assess the two methods for their potential to give different results in a deliberately simple IBM. We show that the two methods produce different results, particularly at high population densities and for increasing interaction complexity (e.g. increasing numbers of trophic levels). This work appears to be the first evidence of the importance of scheduling methods on emergent properties for individual-based models and consequently individually-explicit interactions and behaviour in ecology.

This study aimed to use retirement data from working guide dogs to investigate healthy ageing in dogs and the demographic factors that influence ageing. Using a dataset of 7686 dogs spanning 20 years, dogs withdrawn for health reasons before they reached retirement were identified. Cases of retirement for old age, rather than for health reasons, were also recorded, as was the length of working life for all dogs. Specific health reasons were grouped into 14 different health categories. The influence of purebred or crossbreed, breed, and sex on the incidence of these health categories and the length of working life within each health category was considered.The majority (n = 6465/7686; 84%) of working guide dogs were able to function as guide dogs until they had worked for 8.5 years, when they retired. This working life might constitute a reference for the different breeds considered, with the exception of the German shepherd dog, which had a shorter working life. The most common reason for health withdrawals was musculoskeletal conditions (n = 387/1362; 28%), mostly arthritis. Skin conditions (mostly comprised of cases of atopic dermatitis) reduced working life most commonly (mean, approximately 5 years). Nervous sensory conditions (35% of which were cases of epilepsy) reduced working life by 3 years.

Cost-effective and rigorous risk assessments for chemicals may be based on hazard quotients (HQs): the ratio of a measure of exposure to a substance and a measure of the effect of that substance. HQs have been used for many years in ecological risk assessments for the use of synthetic pesticides in agriculture, and methods for calculating pesticide HQs have been adapted for use with transgenic crops. This paper describes how laboratory methods for assessing the ecotoxicological effects of synthetic pesticides have been modified for the measurement of effects of insecticidal proteins, and how these effect measures are combined with exposure estimates to derive HQs for assessing the ecological risks from the cultivation of insect-resistant transgenic crops. The potential for ecological modeling to inform the design of laboratory effects tests for insecticidal proteins is also discussed.