Adaptation plans have become increasingly popular across the globe. While some adaptations have beneficial outcomes, many have unintended consequences for vulnerability. This is particularly relevant in coastal zones where both marine and land-based adaptations have an impact and human pressures are greatest. We believe a better understanding of the underlying social-ecological processes driving adaptation in coastal areas, particularly the feedbacks between risk from biophysical change, cognitive processes, and adaptation, will reduce the incidence of maladaptations while increasing the frequency of win-win adaptations. Findings will directly inform and support adaptation decision making in coastal areas, add to current knowledge on vulnerability and adaptation, and facilitate learning and appreciation of feedbacks in adaptation responses. We use a model of “private proactive adaptation to climate change” to assess the interactions between: a) the actual risk posed by climate change; b) cognitive factors such as perceived risk and perceived adaptive capacity; c) adaptations; and d) situated learning when decisions makers participate in modelling processes. We assess the relationship between these drivers and adaptation plans in coastal areas at three scales: individual decision makers; local communities of practice; and regional planning authorities. Participatory modelling with decision makers will result in lasting impacts for enhanced coastal resilience. In each of three coastal regions: the Languedoc-Rousillon in France; Cornwall in the UK; and the Garden Route coast in South Africa, we will identify two to three examples where users, communities of practice, and regional authorities have developed adaptation plans and strategies resulting in the unintended transfer of vulnerability from one sector, scale or place to another. We will use available empirical data and models, participatory agent-based modeling, interpretative methods; and reflexive learning to catalyze and assess changes in the cognitive perceptions of decision makers who design adaptation plans.

On coastal reefs (0-50 m depth), perhaps more than anywhere in the world, natural and human systems share a history of strong dependence that must be taken into account to maintain, on one side, the long-term human development and well-being, and, on the other side, biodiversity. This biodiversity translates directly into services. Reef fishes support the nutritional and economic needs of people in many poor countries while hosting the major part of marine life on Earth (25%). However world's reefs are severely over-fished or have degraded habitats. Avoiding or escaping this negative spiral and identifying the most vulnerable reef social-ecological systems on Earth are among the major issues that scientists and managers are facing today. The project aims to move beyond the typical over-simplified ‘human impacts’ storyline and focus on uncovering new solutions based on a prospective and integrated modelling approach of reef social-ecological systems at the global scale with three objectives: 1.Quantifying five key services provided by reef fishes: (i) biomass production providing livelihoods, (ii) nutrient cycling that affects productivity, (iii) regulation of the carbon cycle that affects CO2 concentration, (iv) cultural value that sustains well-being tourism activities and (v) nutritional value insuring food security. 2.Determine the conditions (socioeconomic and environmental) under which these ecosystem services are currently maintained or threatened. Based on a global database of fish surveys over more than 5,000 reefs that encompass wide gradients of environments, human influences (fishing impact), and habitats, we will estimate the boundaries or thresholds beyond which these ecosystem services may collapse. 3.Predict the potential futures of these services and social-ecological systems under various global change scenarios. Using multiple integrated scenarios (human demography, economic development and climate change) and predictive models we will simulate the dynamics of shallow reef ecosystems and their ability to deliver services during the next century.

Reinforcement learning and expectation in the fruit fly, Drosophila melanogaster French participants Overview The fruit fly Drosophila melanogaster has been a valuable model for investigating the genetic and neural bases that underlie learning and memory. Early and most current studies use basic behavior conditioning protocols to study learning in controlled laboratory settings. More recently, the ability to transgenically manipulate many of the brain neurons in the fruit fly with exquisite specificity, and the recent knowledge of the synaptic ‘connectome’ of the fruit fly brain, makes these animals almost unique as a comprehensive model for studies of learning, memory and motivated behavior. In fact, the connectome has revealed many types of new connect ions that had until now been overlooked. Within this context, the thesis of this proposal is that studies of learning and memory will be greatly enhanced by using more sophisticated means for evaluating memory representations, such as have been developed in vertebrates, and combining those studies with information from the connectome guided by computational modelling. We propose to push beyond the boundaries of existing conditioning protocols for fruit flies to investigate more complex memory representations. In particular, we will investigate the function of reinforcement pathways in relation to the absence of expected reinforcement. More specifically, we propose a series of experiments designed to investigate the memory representations in fruit flies when an expected consequence of a Conditioned Stimulus (CS) fails to occur. Although studies have evaluated how this failure can establish extinction memory for the CS, our studies will go beyond studying extinction. Specifically, we predict that in Drosophila when a CS is associated with a failed expectation of an appetitive food reinforcement it will acquire aversive value, and vice versa for a failed expectation of an aversive reinforcer. We combine these studies with manipulations of reinforcement pathways in the CNS inspired from the connectome, iteratively knitted in with established computational models. Intellectual merit The concept of reinforcement expectation and incentive contrast have been influential in the development of studies of associative learning in mammals. These questions are particularly challenging to answer in vertebrates because they require exquisite cellular, temporal, and genetic specificity of experimental manipulations. The recent development of work with identified neurons and their connectomes makes the larval and adult fly brains ripe as models for pushing our understanding of neural bases for these higher-order conditioning phenomena. Broader impacts Public health: These analyses and the conceptual framework of prediction error processing underlying them have a profound impact on our understanding of reinforcementrelated behavior in humans, including monetary rewards and the mnemonic consequences of traumatic experiences, and for pathologies of the dopamine reinforcement system. Educational: This project will provide interdisciplinary training for postdoctoral researchers, Ph.D. and undergraduate students. The PIs will act as co-supervisors or mentors of students working in the different labs via face-to-face and internet-based technologies. We will also work with ASU’s award-winning Ask-A-Biologist program. This is an online science program designed to enrich the learning experiences of students of all ages and to provide classroom material for use by K-12 teachers. We will develop an extension of a game developed under a prior NSF award, and the new game will include modules to teach K-12 students about how insects learn. We will also integrate into the AAB site a program developed at the Leibniz Institut für Neurobiologie, Magdeburg, and now in use in schools in Germany, to teach K-12 students how to train animals using the fruit fly larval learning paradigm.

Decades of research on earthquakes have yielded meager prospects for earthquake predictability: we cannot predict the time, location and magnitude of a forthcoming earthquake with sufficient accuracy for immediate societal value. Therefore, the best we can do is to mitigate their impact by anticipating the most “destructive properties” of the largest earthquakes to come: longest extent of rupture zones, largest magnitudes, amplitudes of displacements, accelerations of the ground. This topic has motivated many studies in last decades. Yet, despite these efforts, major discrepancies still remain between available model outputs and natural earthquake behaviors. Here we argue that an important source of discrepancy is related to the incomplete integration of actual geometrical and mechanical properties of earthquake causative faults in existing rupture models. Indeed, our group has been among the pioneers to show that faults are 3D features, systematically embedded in a permanent damage zone where crustal rocks are intensely faulted and hence are compliant. Faults also are systematically segmented laterally in a generic manner, and this segmentation divides their planes and produces strength and stress heterogeneities in a deterministic manner. As faults grow over the long-term and become more “mature”, some of their properties evolve: the damage zone enlarges and its compliance increases, the fault segments become more tightly connected, the fault plane roughness decreases as might also do the fault friction. All these fault properties and their changes in relation to fault maturity markedly modify the earthquake behavior. In particular, earthquakes on mature and immature faults produce different amplitudes of slips and ground motions, whereas earthquake slips and speeds are systematically largest on the most mature sections of the ruptured zones. These intimate connections between fault and earthquake properties mean that a synoptic understanding of earthquake mechanics cannot be successful until it more fully includes actual fault properties. This is not done at present, as most current earthquake models either ignore fault properties or oversimplify them. We thus aim to take benefit of the fault data and knowledge we have gained in the last decades, and of our strong experience in earthquake modeling, to develop a new generation of rupture and ground motion (GM) models, based on a novel paradigm: 3D fault zones with generic macroscopic properties (especially permanent damage and lateral segmentation) whose inhomogeneous and anisotropic characters evolve depending on both overall and along-strike fault maturity. Furthermore, since most major fault properties are deterministic, even generic, they must result from some common, scale-invariant physics. The understanding of that physics should advance generic earthquake and GM models that could be run for the vast majority of faults and earthquakes worldwide. These new models should thus open a novel avenue in earthquake modeling. We first aim to document the compliance of rocks in natural permanent damage zones. These data –key to earthquake modeling– are presently lacking. A second objective is to introduce the observed macroscopic fault properties –compliant permanent damage, segmentation, maturity– into 3D dynamic earthquake models we have developed in prior works. A third objective is to compute Ground Motions (GM) from these new fault-based earthquake models and propagate them into non-linear media, using codes that we have developed. A fourth objective is to conduct a pilot study aiming at examining the gain of prior fault property and rupture scenario knowledge for Earthquake Early Warning (EEW). We expect that integrating actual fault properties in dynamic rupture, GM, and EEW models will decrease the discrepancies between models outputs and natural earthquake behavior, and hence allow a more accurate anticipation of the “destructive properties” of forthcoming events.