Conservation planning for adaptive and neutral evolutionary processes

Protected area systems should ideally maintain adaptive and neutral evolutionary processes. To achieve this, plans for expanding protected area systems (prioritizations) can improve coverage of related attributes (evolutionary attributes). However, long-standing challenges in mapping and operationalizing evolutionary attributes have prevented their widespread usage. We outline a novel framework for incorporating evolutionary processes into conservation planning. Using three amphibian species in the Iberian Peninsula (Hyla molleri, Pelobates cultripes and Rana iberica), we mapped a comprehensive range of adaptive and neutral evolutionary attributes to delineate places containing individuals with moderate to high heterozygosity, different neutral genetic clusters, different adaptive genetic clusters and climatic refugia. We overlaid these maps with boundaries of existing protected areas to quantify representation shortfalls and generated a prioritization to identify additional priority areas. To assess the performance of conventional approaches, we also generated a prioritization using only the species' distribution data—without the evolutionary attributes. We found that existing protected areas within the Iberian Peninsula are failing to adequately represent evolutionary attributes for the study species. Specifically, they are not adequately representing places predicted to contain individuals with moderate to high heterozygosity for any of the studied species, and neither are they adequately representing the species' potential climatic refugia. They also have poor coverage of the distinct adaptive and neutral genetic clusters that comprise each of the species' distributions. By incorporating the evolutionary attributes into the prioritization process, we identified priority areas that would address all of the shortfalls for only a minor increase in the size of the protected area system. In comparison, the prioritization generated following conventional approaches, despite encompassing a similar extent, did not substantially improve representation of the species' evolutionary attributes. Synthesis and applications. We introduce a framework for incorporating adaptive and neutral evolutionary processes into conservation planning. This framework can reveal weaknesses in the coverage of climatic refugia, genetic diversity and potential local adaptations by existing protected areas. Moreover, it can identify priority areas to improve conservation of evolutionary processes. Since neglecting evolutionary processes can impair conservation plans, we recommend using evolutionary data to inform decision-making where possible.This work was developed under the project PTDC/BIA-BIC/3545/2014, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). Support by Portuguese National funds through Fundação para a Ciência e Tecnologia (FCT) was granted to AV (DL57/2016) and SBC (individual scientific employment program-contract CEECIND/01464/2017). GVA was supported by Investigador FCT contract (IF/01425/2014)

Autonomous Upper Stage Guidance Using Convex Optimization and Model Predictive Control

This paper proposes a novel algorithm, based on model predictive control (MPC), for the optimal guidance of a launch vehicle upper stage. The guidance algorithm must take into account a realistic dynamical model and several nonconvex constraints, such as the maximum heat flux after fairing jettisoning and the splash-down of the burned-out stage, to properly predict and optimize the system performance. Convex optimization is embedded into the MPC framework to allow for high update frequencies. Specifically, state-of-the-art convexification methods and a hp pseudospectral discretization scheme are used to formulate the optimal control problem as a sequence of second-order cone programming problems that quickly converges to an optimal solution. Convergence is further enhanced via a soft trust region and an improved strategy for updating the reference solution. Also, virtual controls and proper constraint relaxations are introduced to guarantee the recursive feasibility of the algorithm. Numerical results relative to the autonomous guidance of the third stage of a VEGA-like vehicle are presented to prove the validity of the MPC approach. The computational efficiency and robustness of the algorithm are discussed on the basis of extensive Monte Carlo campaigns that account for off-nominal initial conditions and random in-flight disturbances