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A vast number of prioritization schemes have been developed to help conservation navigate tough decisions about the allocation of finite resources. However, the application of quantitative approaches to setting priorities in conservation frequently includes mistakes that can undermine their authors' intention to be more rigorous and scientific in the way priorities are established and resources allocated. Drawing on well-established principles of decision science, we highlight 6 mistakes commonly associated with setting priorities for conservation: not acknowledging conservation plans are prioritizations; trying to solve an illdefined problem; not prioritizing actions; arbitrariness; hidden value judgments; and not acknowledging risk of failure. We explain these mistakes and offer a path to help conservation planners avoid making the same mistakes in future prioritizations. Se ha desarrollado un vasto número de esquemas de priorización para ayudar a que la conservación navegue entre decisiones difíciles en cuanto a la asignación de recursos finitos. Sin embargo, la aplicación de métodos cuantitativos para la definición de prioridades en la conservación frecuentemente incluye errores que pueden socavar la intención de sus autores de ser más rigurosos y científicos en la manera en que se establecen las prioridades y se asignan los recursos. Con base en los bien establecidos principios de la ciencia de la decisión, resaltamos seis errores comúnmente asociados con la definición de prioridades para la conservación: no reconocer que los planes de conservación son priorizaciones; tratar de resolver un problema mal definido; no priorizar acciones; arbitrariedad; juicios de valor ocultos y no reconocer el riesgo de fracasar. Explicamos estos errores y ofrecemos un camino para que planificadores de la conservación no cometan los mismos errores en priorizaciones futuras.

Why it is important to plan for the natural environment at a whole landscape scale and to connect wildlife habitats together? Why do planners need to look beyond protecting particular species and their habitats? Why should planners help nature to recolonise towns and cities and how best can they do this? 0In seeking to answer such questions, this book provides a grounding for planners and other related professionals in the key concepts associated with biodiversity and the natural environment, and in how to apply them in practice. It looks at how natural environment policy has shifted from the protection of rare species and nature reserves to a more holistic approach, based on biodiversity.

Explicitly including cost in marine conservation planning is essential for achieving feasible and efficient conservation outcomes. Yet, spatial priorities for marine conservation are still often based solely on biodiversity hotspots, species richness, and/or cumulative threat maps. This study aims to provide an approach for including cost when planning large-scale Marine Protected Area (MPA) networks that span multiple countries. Here, we explore the incorporation of cost in the complex setting of the Mediterranean Sea. In order to include cost in conservation prioritization, we developed surrogates that account for revenue from multiple marine sectors: commercial fishing, noncommercial fishing, and aquaculture. Such revenue can translate into an opportunity cost for the implementation of an MPA network. Using the software Marxan, we set conservation targets to protect 10% of the distribution of 77 threatened marine species in the Mediterranean Sea. We compared nine scenarios of opportunity cost by calculating the area and cost required to meet our targets. We further compared our spatial priorities with those that are considered consensus areas by several proposed prioritization schemes in the Mediterranean Sea, none of which explicitly considers cost. We found that for less than 10% of the Sea's area, our conservation targets can be achieved while incurring opportunity costs of less than 1%. In marine systems, we reveal that area is a poor cost surrogate and that the most effective surrogates are those that account for multiple sectors or stakeholders. Furthermore, our results indicate that including cost can greatly influence the selection of spatial priorities for marine conservation of threatened species. Although there are known limitations in multinational large-scale planning, attempting to devise more systematic and rigorous planning methods is especially critical given that collaborative conservation action is on the rise and global financial crisis restricts conservation investments.

Readers will find out what urban rewilding is and how it can make our lives (and our planet) safer and healthier.

The Earth's oceans are inherently 3‐D in nature. Many physical, environmental, and biotic processes vary widely across depths. In recent years, human activities, such as oil drilling, mining, and fishing are rapidly expanding into deeper frontier ocean areas, where much of the biodiversity remains unknown. Most current conservation actions, management decisions and policies of both the pelagic and benthic domains do not explicitly incorporate the 3‐D nature of the oceans and are still based on a two‐dimensional approach. Here, we review current advances in marine research and conservation, aiming to advance towards incorporating the third dimension in marine systematic conservation planning. We highlight the importance and potential of vertical conservation planning and zoning from the sea surface to the seafloor. We propose that undertaking marine conservation, management and environmental decisions in 3‐D has the potential to revolutionize marine conservation research, practice and legislation.

The conservation of our architectural heritage is a long-running subject of debate. When do buildings become old enough to warrant special heritage status and protection? Who decides which buildings are historically significant enough to protect and conserve? And what does this mean for building users and owners, who might well be better served if their buildings were less authentic, but more comfortable and usable? Presenting a clear line of sight through these knotty problems, this book explores the conservation, regeneration and adaptive re-use of modern architecture. It provides a general grounding in the field, its recent history and current development, including chapters on authenticity, listing and protection. Case studies drawing on the author's extensive practical experience offer valuable lessons learnt in the conservation of heritage buildings.

Despite the committed action by many in past decades, recent reviews show little progress in slowing species declines, and future waves of extinction are predicted. Not only do such declines signal a failure to meet international commitments to stem biodiversity loss and undermine the potential for achievement of the species-related target in the Post-2020 Biodiversity Framework, but they also jeopardize our ability to achieve the 2030 Sustainable Development Goals, many of which rely on the resources provided by species and the ecosystems they support. A substantial increase in ambition and the application of tools at the global scale and across all elements of the species conservation cycle—Assess, Plan, and Act—is urgently needed to create swift and lasting positive change for species. Well-resourced, effectively implemented species conservation plans play a key role in meeting this challenge. Here, the IUCN SSC Conservation Planning Specialist Group (CPSG) presents a proven approach to species conservation planning that emphasizes the thoughtful design and facilitation of collaborative processes that feature the rigorous scientific analysis of quantitative data on species biology and impacts of anthropogenic threats and their mitigation through management. When incorporated from the beginning of a species management project, the CPSG’s principles and steps for conservation planning can help reverse the decline of threatened species.

The United Nations has declared 2021–2030 the decade on ecosystem restoration with the aim of preventing, stopping and reversing the degradation of the ecosystems of the world, often caused by the fragmentation of natural landscapes. Human activities separate and surround habitats, making them too small to sustain viable animal populations or too far apart to enable foraging and gene flow. Despite the need for strategies to solve fragmentation, it remains unclear how to efficiently reconnect nature. In this paper, we illustrate the potential of deep reinforcement learning (DRL) to tackle the spatial optimisation aspect of connectivity conservation planning. The propensity of spatial optimisation problems to explode in complexity depending on the number of input variables and their states is and will continue to be one of its most serious obstacles. DRL is an emerging class of methods focused on training deep neural networks to solve decision‐making tasks and has been used to learn good heuristics for complex optimisation problems. While the potential of DRL to optimise conservation decisions seems huge, only few examples of its application exist. We applied DRL to two real‐world raster datasets in a connectivity planning setting, targeting graph‐based connectivity indices for optimisation. We show that DRL converges to the known optimums in a small example where the objective is the overall improvement of the Integral Index of Connectivity and the only constraint is the budget. We also show that DRL approximates high‐quality solutions on a large example with additional cost and spatial configuration constraints where the more complex Probability of Connectivity Index is targeted. To the best of our knowledge, there is no software that can target this index for optimisation on raster data of this size. DRL can be used to approximate good solutions in complex spatial optimisation problems even when the conservation feature is non‐linear like graph‐based indices. Furthermore, our methodology decouples the optimisation process and the index calculation, so it can potentially target any other conservation feature implemented in current or future software. Las Naciones Unidas han declarado 2021–2030 la década para la restauración ecológica, con el objetivo de prevenir, detener e incluso revertir la degradación de los ecosistemas del mundo. Esta alteración es causada a menudo por la fragmentación de los paisajes naturales. Las actividades humanas dividen y aíslan los hábitats, haciéndolos demasiado pequeños para sustentar poblaciones animales viables o demasiado separados para permitir el forrajeo y el flujo genético. A pesar de la necesidad de estrategias para resolver la fragmentación, sigue sin ser claro cómo reconectar eficazmente a la naturaleza. En este artículo, ilustramos el potencial del Aprendizaje Profundo por Refuerzo (APR) para abordar el aspecto de optimización espacial en la planificación de la conservación de la conectividad. La propensión de los problemas de optimización espacial a crecer exponencialmente en complejidad en función del número de variables y sus estados es, y seguirá siendo, uno de sus obstáculos más serios. El APR es una clase de métodos para el entrenamiento de redes neuronales profundas con el fin de resolver tareas de toma de decisiones y se ha utilizado para diseñar buenas heurísticas para problemas de optimización complejos. Si bien el potencial de el APR para optimizar las decisiones de conservación parece enorme, actualmente sólo existen unos pocos ejemplos de su aplicación. En este estudio, aplicamos APR a dos rásteres de cobertura del suelo del mundo real en un entorno de planificación de conectividad, apuntando a la optimización de índices de conectividad basados en grafos. Mostramos que APR converge a los óptimos conocidos en un ejemplo pequeño donde el objetivo es la mejora del Índice Integral de Conectividad y la única restricción es el presupuesto. También, mostramos que APR se aproxima a soluciones de alta calidad en un ejemplo mayor, con restricciones adicionales de costos y de configuración espacial y donde el objetivo es la mejora del Índice de Probabilidad de Conectividad. Hasta donde sabemos, no existe ningún software que pueda optimizar este índice sobre datos ráster del tamaño que nosotros procesamos. El APR puede utilizarse para aproximar buenas soluciones en problemas complejos de optimización espacial, incluso cuando el objetivo de conservación es no lineal, como lo son los índices basados en grafos. Además, nuestra metodología desvincula el proceso de optimización y el cálculo del índice, por lo que potencialmente puede incorporar cualquier otro objetivo de conservación implementado en el software actual o futuro.