Accounting for direct and indirect cumulative effects of anthropogenic pressures on salmon- and herring-linked land and ocean ecosystems.

Salmon and herring support both land and ocean predators and are critical to ecosystem resilience. Their linkages across land and sea realms make them highly susceptible to human activities, which can have flow-on effects up the food web. We quantify and compare the potential cumulative effects of human-driven pressures on interdependent species in salmon- and herring-linked ecosystems of western Canada using a risk assessment methodology. Adding indirect risks resulted in 68% greater total risks for land species than for direct risk alone, versus 15% for marine species. Inclusion of climate change pressures resulted in the greatest change in risk for low trophic marine species and habitats (greater than 25% increase). Forestry-related pressures accounted for the highest risk to all species and projected management of these pressures resulted in a total reduction of risk across all ecosystem components that was more than 14% greater than management of fisheries pressures. Ignoring land food web linkages and pressures underestimated cumulative risk by more than 40% for salmon and herring. This simple framework can be used to evaluate potential risk of existing human uses and future change to inform immediate management of linked land-sea ecosystems and help species avoid the 'death by a thousand cuts'. This article is part of the theme issue 'Nurturing resilient marine ecosystems'.

An introduction to decision science for conservation.

Biodiversity conservation decisions are difficult, especially when they involve differing values, complex multidimensional objectives, scarce resources, urgency, and considerable uncertainty. Decision science embodies a theory about how to make difficult decisions and an extensive array of frameworks and tools that make that theory practical. We sought to improve conceptual clarity and practical application of decision science to help decision makers apply decision science to conservation problems. We addressed barriers to the uptake of decision science, including a lack of training and awareness of decision science; confusion over common terminology and which tools and frameworks to apply; and the mistaken impression that applying decision science must be time consuming, expensive, and complex. To aid in navigating the extensive and disparate decision science literature, we clarify meaning of common terms: decision science, decision theory, decision analysis, structured decision-making, and decision-support tools. Applying decision science does not have to be complex or time consuming; rather, it begins with knowing how to think through the components of a decision utilizing decision analysis (i.e., define the problem, elicit objectives, develop alternatives, estimate consequences, and perform trade-offs). This is best achieved by applying a rapid-prototyping approach. At each step, decision-support tools can provide additional insight and clarity, whereas decision-support frameworks (e.g., priority threat management and systematic conservation planning) can aid navigation of multiple steps of a decision analysis for particular contexts. We summarize key decision-support frameworks and tools and describe to which step of a decision analysis, and to which contexts, each is most useful to apply. Our introduction to decision science will aid in contextualizing current approaches and new developments, and help decision makers begin to apply decision science to conservation problems.

Las decisiones sobre la conservación de la biodiversidad son difíciles de tomar, especialmente cuando involucran diferentes valores, objetivos multidimensionales complejos, recursos limitados, urgencia y una incertidumbre considerable. Las ciencias de la decisión incorporan una teoría sobre cómo tomar decisiones difíciles y una variedad extensa de marcos de trabajo y herramientas que transforman esa teoría en práctica. Buscamos mejorar la claridad conceptual y la aplicación práctica de las ciencias de la decisión para ayudar al órgano decisorio a aplicar estas ciencias a los problemas de conservación. Nos enfocamos en las barreras para la aceptación de las ciencias de la decisión, incluyendo la falta de capacitación y de conciencia por estas ciencias; la confusión por la terminología común y cuáles herramientas y marcos de trabajo aplicar; y la impresión errónea de que la aplicación de estas ciencias consume tiempo y debe ser costosa y compleja. Para asistir en la navegación de la literatura extensa y dispar de las ciencias de la decisión, aclaramos el significado de varios términos comunes: ciencias de la decisión, teoría de la decisión, análisis de decisiones, toma estructurada de decisiones y herramientas de apoyo para las decisiones. La aplicación de las ciencias de la decisión no tiene que ser compleja ni debe llevar mucho tiempo; de hecho, todo comienza con saber cómo pensar detenidamente en los componentes de una decisión mediante el análisis de decisiones (es decir, definir el problema, producir objetivos, desarrollar alternativas, estimar consecuencias y realizar compensaciones). Lo anterior se logra de mejor manera mediante la aplicación de una estrategia prototipos rápidos. En cada paso, las herramientas de apoyo para las decisiones pueden proporcionar visión y claridad adicionales, mientras que los marcos de apoyo para las decisiones (p.ej.: gestión de amenazas prioritarias y planeación sistemática de la conservación) pueden asistir en la navegación de los diferentes pasos de un análisis de decisiones para contextos particulares. Resumimos los marcos de trabajo y las herramientas más importantes de apoyo para las decisiones y describimos el paso, y el contexto, del análisis de decisiones para el que es más útil aplicarlos. Nuestra introducción a las ciencias de la decisión apoyará en la contextualización de las estrategias actuales y los nuevos desarrollos, y ayudarán al órgano decisorio a comenzar a aplicar estas ciencias en los problemas de conservación.

Identifying management opportunities to combat climate, land, and marine threats across less climate exposed coral reefs.

Conserving coral reefs is critical for maintaining marine biodiversity, protecting coastlines, and supporting livelihoods in many coastal communities. Climate change threatens coral reefs globally, but researchers have identified a portfolio of coral reefs (bioclimatic units [BCUs]) that are relatively less exposed to climate impacts and strongly connected to other coral reef systems. These reefs provide a proactive opportunity to secure a long-term future for coral reefs under climate change. To help guide local management efforts, we quantified marine cumulative human impact (CHI) from climate, marine, and land pressures (2013 and from 2008 to 2013) in BCUs and across countries tasked with BCU management. Additionally, we created a management index based on common management measures and policies for each pressure source (climate, marine, and land) to identify a country's intent and commitment to effectively manage these pressures. Twenty-two countries (79%) had increases in CHI from 2008 to 2013. Climate change pressures had the highest proportional contribution to CHI across all reefs and in all but one country (Singapore), but 18 BCUs (35%) and nine countries containing BCUs (32%) had relatively high land and marine impacts. There was a significant positive relationship between climate impact and the climate management index across countries (R2 = 0.43, p = 0.02), potentially signifying that countries with greater climate impacts are more committed to managing them. However, this trend was driven by climate management intent in Fiji and Bangladesh. Our results can be used to guide future fine-scale analyses, national policies, and local management decisions, and our management indices reveal areas where management components can be improved. Cost-effectively managing local pressures (e.g., fishing and nutrients) in BCUs is essential for building a climate-ready future that benefits coral reefs and people.

Identificación de Oportunidades de Gestión para Combatir las Amenazas Climáticas, Marinas y Terrestres en los Arrecifes de Coral Menos Expuestos al Clima Resumen La conservación de los arrecifes de coral es de suma importancia para mantener la biodiversidad marina y para sostener el medio de vida en muchas comunidades costeras. El cambio climático es una amenaza mundial para los arrecifes de coral; aun así, los investigadores han identificado un portafolio de arrecifes de coral (unidades bioclimáticas[UBCs]) que se encuentran relativamente menos expuestos a los impactos climáticos y están conectados a otros sistemas arrecifales. Estos arrecifes proporcionan una oportunidad proactiva de asegurar un futuro a largo plazo para los arrecifes de coral frente al cambio climático. Para ayudar a guiar los esfuerzos locales de manejo, cuantificamos el impacto humano acumulativo (IHA) sobre los ambientes marinos a partir de las presiones climáticas, marinas y terrestres (del 2008 al 2013) en las UBCs y en los países encargados del manejo de estas. Además, creamos un índice de manejo con base en las medidas y políticas comunes de gestión para cada fuente de presión (clima, ambiente marino, suelo) para identificar la intención y el compromiso de cada país para manejar de manera efectiva estas presiones. Veintidós países (79%) tuvieron incrementos en el IHA entre 2008 y 2013. Las presiones por el cambio climático tuvieron la contribución proporcional más alta al IHA en todos los arrecifes y en todos los países excepto uno (Singapur), pero 18 UBCs (35%) y nueve países que cuentan con UBCs (32%) tuvieron impactos terrestres y marinos relativamente altos. Hubo una relación positiva significativa entre el impacto climático y el índice de manejo climático entre los países (R2 = 0.43, p = 0.02), lo que potencialmente significa que los países con un mayor impacto climático están más comprometidos con su manejo. Sin embargo, esta tendencia estuvo impulsada por las intenciones de manejo climático en Fiyi y en Bangladesh. Nuestros resultados pueden usarse para orientar los análisis a fina escala, las políticas nacionales y las decisiones locales de manejo en el futuro. Nuestros índices de manejo también revelan áreas en donde se pueden mejorar los componentes gestores. El manejo rentable de las presiones locales (p. ej.: la pesca, los nutrientes) dentro de las UBCs es esencial para construir un futuro preparado climáticamente que beneficie a los arrecifes y a las personas.

Anthropogenic pressures and life history predict trajectories of seagrass meadow extent at a global scale.

Seagrass meadows are threatened by multiple pressures, jeopardizing the many benefits they provide to humanity and biodiversity, including climate regulation and food provision through fisheries production. Conservation of seagrass requires identification of the main pressures contributing to loss and the regions most at risk of ongoing loss. Here, we model trajectories of seagrass change at the global scale and show they are related to multiple anthropogenic pressures but that trajectories vary widely with seagrass life-history strategies. Rapidly declining trajectories of seagrass meadow extent (>25% loss from 2000 to 2010) were most strongly associated with high pressures from destructive demersal fishing and poor water quality. Conversely, seagrass meadow extent was more likely to be increasing when these two pressures were low. Meadows dominated by seagrasses with persistent life-history strategies tended to have slowly changing or stable trajectories, while those with opportunistic species were more variable, with a higher probability of either rapidly declining or rapidly increasing. Global predictions of regions most at risk for decline show high-risk areas in Europe, North America, Japan, and southeast Asia, including places where comprehensive long-term monitoring data are lacking. Our results highlight where seagrass loss may be occurring unnoticed and where urgent conservation interventions are required to reverse loss and sustain their essential services.

Software for prioritizing conservation actions based on probabilistic information.

Marxan is the most common decision-support tool used to inform the design of protected-area systems. The original version of Marxan does not consider risk and uncertainty associated with threatening processes affecting protected areas, including uncertainty about the location and condition of species' populations and habitats now and in the future. We described and examined the functionality of a modified version of Marxan, Marxan with Probability. This software explicitly considers 4 types of uncertainty: probability that a feature exists in a particular place (estimated based on species distribution models or spatially explicit population models); probability that features in a site will be lost in the future due to a threatening process, such as climate change, natural catastrophes, and uncontrolled human interventions; probability that a feature will exist in the future due to natural successional processes, such as a fire or flood; and probability the feature exists but has been degraded by threatening processes, such as overfishing or pollution, and thus cannot contribute to conservation goals. We summarized the results of 5 studies that illustrate how each type of uncertainty can be used to inform protected area design. If there were uncertainty in species or habitat distribution, users could maximize the chance that these features were represented by including uncertainty using Marxan with Probability. Similarly, if threatening processes were considered, users minimized the chance that species or habitats were lost or degraded by using Marxan with Probability. Marxan with Probability opens up substantial new avenues for systematic conservation planning research and application by agencies.

Marxan es la herramienta de apoyo a las decisiones que más comúnmente se usa para orientar el diseño de los sistemas de áreas protegidas. La versión original de Marxan no considera el riesgo y la incertidumbre asociados con los procesos amenazantes que afectan a las áreas protegidas, incluyendo la incertidumbre sobre la ubicación y la condición de las poblaciones de las especies y su hábitat ahora y en el futuro. Describimos y analizamos la funcionalidad de una versión modificada de Marxan: Marxan con Probabilidad. Este software considera explícitamente cuatro tipos de incertidumbre: probabilidad de que una característica exista en un lugar en particular (estimada con base en los modelos de distribución de especies o con modelos de población espacialmente explícitos); probabilidad de que las características de un sitio se pierdan en el futuro debido a un proceso amenazante, como el cambio climático, las catástrofes naturales y las intervenciones humanas descontroladas; probabilidad de que una característica existirá en el futuro debido a los procesos naturales de sucesión; como los incendios o las inundaciones; y probabilidad de que una característica exista pero haya sido degradada por los procesos amenazantes, como la sobrepesca y la contaminación, y por lo tanto no puede contribuir a los objetivos de conservación. Sintetizamos los resultados de cinco estudios que ilustraron cómo cada tipo de incertidumbre puede usarse para orientar el diseño del área protegida. Si hubiera incertidumbre en la distribución de la especie o de su hábitat, los usuarios podrían maximizar la posibilidad de que estas características estuvieran representadas mediante la inclusión de Marxan con Probabilidad. De manera similar, si los procesos amenazantes estuvieran considerados, los usuarios minimizarían la posibilidad de que se pierda la especie o degrade el hábitat usando Marxan con Probabilidad. Marxan con Probabilidad abre nuevas vías importantes para la investigación sobre la planeación sistemática de la conservación y su aplicación por parte de las agencias.

Artificial Intelligence Meets Citizen Science to Supercharge Ecological Monitoring.

The development and uptake of citizen science and artificial intelligence (AI) techniques for ecological monitoring is increasing rapidly. Citizen science and AI allow scientists to create and process larger volumes of data than possible with conventional methods. However, managers of large ecological monitoring projects have little guidance on whether citizen science, AI, or both, best suit their resource capacity and objectives. To highlight the benefits of integrating the two techniques and guide future implementation by managers, we explore the opportunities, challenges, and complementarities of using citizen science and AI for ecological monitoring. We identify project attributes to consider when implementing these techniques and suggest that financial resources, engagement, participant training, technical expertise, and subject charisma and identification are important project considerations. Ultimately, we highlight that integration can supercharge outcomes for ecological monitoring, enhancing cost-efficiency, accuracy, and multi-sector engagement.

A guide to ecosystem models and their environmental applications.

Applied ecology has traditionally approached management problems through a simplified, single-species lens. Repeated failures of single-species management have led us to a new paradigm - managing at the ecosystem level. Ecosystem management involves a complex array of interacting organisms, processes and scientific disciplines. Accounting for interactions, feedback loops and dependencies between ecosystem components is therefore fundamental to understanding and managing ecosystems. We provide an overview of the main types of ecosystem models and their uses, and discuss challenges related to modelling complex ecological systems. Existing modelling approaches typically attempt to do one or more of the following: describe and disentangle ecosystem components and interactions; make predictions about future ecosystem states; and inform decision making by comparing alternative strategies and identifying important uncertainties. Modelling ecosystems is challenging, particularly when balancing the desire to represent many components of an ecosystem with the limitations of available data and the modelling objective. Explicitly considering different forms of uncertainty is therefore a primary concern. We provide some recommended strategies (such as ensemble ecosystem models and multi-model approaches) to aid the explicit consideration of uncertainty while also meeting the challenges of modelling ecosystems.

The Role of Vegetated Coastal Wetlands for Marine Megafauna Conservation.

Habitat loss is accelerating a global extinction crisis. Conservation requires understanding links between species and habitats. Emerging research is revealing important associations between vegetated coastal wetlands and marine megafauna, such as cetaceans, sea turtles, and sharks. But these links have not been reviewed and the importance of these globally declining habitats is undervalued. Here, we identify associations for 102 marine megafauna species that utilize these habitats, increasing the number of species with associations based on current International Union for the Conservation of Nature (IUCN) species assessments by 59% to 174, accounting for over 13% of all marine megafauna. We conclude that coastal wetlands require greater protection to support marine megafauna, and present a simple, effective framework to improve the inclusion of habitat associations within species assessments.

Future recovery of baleen whales is imperiled by climate change.

Historical harvesting pushed many whale species to the brink of extinction. Although most Southern Hemisphere populations are slowly recovering, the influence of future climate change on their recovery remains unknown. We investigate the impacts of two anthropogenic pressures-historical commercial whaling and future climate change-on populations of baleen whales (blue, fin, humpback, Antarctic minke, southern right) and their prey (krill and copepods) in the Southern Ocean. We use a climate-biological coupled "Model of Intermediate Complexity for Ecosystem Assessments" (MICE) that links krill and whale population dynamics with climate change drivers, including changes in ocean temperature, primary productivity and sea ice. Models predict negative future impacts of climate change on krill and all whale species, although the magnitude of impacts on whales differs among populations. Despite initial recovery from historical whaling, models predict concerning declines under climate change, even local extinctions by 2100, for Pacific populations of blue, fin and southern right whales, and Atlantic/Indian fin and humpback whales. Predicted declines were a consequence of reduced prey (copepods/krill) from warming and increasing interspecific competition between whale species. We model whale population recovery under an alternative scenario whereby whales adapt their migratory patterns to accommodate changing sea ice in the Antarctic and a shifting prey base. Plasticity in range size and migration was predicted to improve recovery for ice-associated blue and minke whales. Our study highlights the need for ongoing protection to help depleted whale populations recover, as well as local management to ensure the krill prey base remains viable, but this may have limited success without immediate action to reduce emissions.

The value of using feasibility models in systematic conservation planning to predict landholder management uptake.

Understanding the social dimensions of conservation opportunity is crucial for conservation planning in multiple-use landscapes. However, factors that influence the feasibility of implementing conservation actions, such as the history of landscape management, and landholders' willingness to engage are often difficult or time consuming to quantify and rarely incorporated into planning. We examined how conservation agencies could reduce costs of acquiring such data by developing predictive models of management feasibility parameterized with social and biophysical factors likely to influence landholders' decisions to engage in management. To test the utility of our best-supported model, we developed 4 alternative investment scenarios based on different input data for conservation planning: social data only; biological data only; potential conservation opportunity derived from modeled feasibility that incurs no social data collection costs; and existing conservation opportunity derived from feasibility data that incurred collection costs. Using spatially explicit information on biodiversity values, feasibility, and management costs, we prioritized locations in southwest Australia to control an invasive predator that is detrimental to both agriculture and natural ecosystems: the red fox (Vulpes vulpes). When social data collection costs were moderate to high, the most cost-effective investment scenario resulted from a predictive model of feasibility. Combining empirical feasibility data with biological data was more cost-effective for prioritizing management when social data collection costs were low (<4% of the total budget). Calls for more data to inform conservation planning should take into account the costs and benefits of collecting and using social data to ensure that limited funding for conservation is spent in the most cost-efficient and effective manner.