Roles of the Red List of Ecosystems in the Kunming-Montreal Global Biodiversity Framework.

The Kunming-Montreal Global Biodiversity Framework (GBF) of the UN Convention on Biological Diversity set the agenda for global aspirations and action to reverse biodiversity loss. The GBF includes an explicit goal for maintaining and restoring biodiversity, encompassing ecosystems, species and genetic diversity (goal A), targets for ecosystem protection and restoration and headline indicators to track progress and guide action1. One of the headline indicators is the Red List of Ecosystems2, the global standard for ecosystem risk assessment. The Red List of Ecosystems provides a systematic framework for collating, analysing and synthesizing data on ecosystems, including their distribution, integrity and risk of collapse3. Here, we examine how it can contribute to implementing the GBF, as well as monitoring progress. We find that the Red List of Ecosystems provides common theory and practical data, while fostering collaboration, cross-sector cooperation and knowledge sharing, with important roles in 16 of the 23 targets. In particular, ecosystem maps, descriptions and risk categories are key to spatial planning for halting loss, restoration and protection (targets 1, 2 and 3). The Red List of Ecosystems is therefore well-placed to aid Parties to the GBF as they assess, plan and act to achieve the targets and goals. We outline future work to further strengthen this potential and improve biodiversity outcomes, including expanding spatial coverage of Red List of Ecosystems assessments and partnerships between practitioners, policy-makers and scientists.

Establishing the Minimum Media Time Sample Required to Obtain Reliable Estimates of Children's Digital Media Food Marketing Exposures.

Background: The ubiquitous nature of food marketing on digital media likely has a profound effect on children's food preferences and intake. Monitoring children's exposure to digital marketing is necessary to raise awareness of the issue, inform policy development, and evaluate policy implementation and effect. Objectives: This study aimed to establish whether smaller time samples (less time and/or fewer days captured) would provide robust estimates of children's usual exposures to food marketing. Methods: Using an existing data set of children's digital marketing exposures, which captured children's total screen use over 3 d, a reliability assessment was performed. Results: A subsample of 30% of children's usual screen time was found to provide reliable estimates of digital food marketing exposure compared with the full sample (intraclass correlation coefficient: 0.885; Cronbach α: 0.884). There was no difference in the rates of marketing (exposures/h) between weekdays and weekend days. Conclusions: These findings enable researchers to reduce the time and resource constraints that have previously restricted this type of monitoring research. The reduced media time sample will further lessen participant burden.

A function-based typology for Earth's ecosystems.

As the United Nations develops a post-2020 global biodiversity framework for the Convention on Biological Diversity, attention is focusing on how new goals and targets for ecosystem conservation might serve its vision of 'living in harmony with nature'1,2. Advancing dual imperatives to conserve biodiversity and sustain ecosystem services requires reliable and resilient generalizations and predictions about ecosystem responses to environmental change and management3. Ecosystems vary in their biota4, service provision5 and relative exposure to risks6, yet there is no globally consistent classification of ecosystems that reflects functional responses to change and management. This hampers progress on developing conservation targets and sustainability goals. Here we present the International Union for Conservation of Nature (IUCN) Global Ecosystem Typology, a conceptually robust, scalable, spatially explicit approach for generalizations and predictions about functions, biota, risks and management remedies across the entire biosphere. The outcome of a major cross-disciplinary collaboration, this novel framework places all of Earth's ecosystems into a unifying theoretical context to guide the transformation of ecosystem policy and management from global to local scales. This new information infrastructure will support knowledge transfer for ecosystem-specific management and restoration, globally standardized ecosystem risk assessments, natural capital accounting and progress on the post-2020 global biodiversity framework.

Rootstock, Vine Vigor, and Light Mediate Methoxypyrazine Concentrations in the Grape Bunch Rachis of Vitis vinifera L. cv. Cabernet Sauvignon.

Ramsey rootstock has previously been implicated in an approximate 8-fold increase of 3-isobutyl-2-methoxypyrazine (IBMP) levels in the rachis (grape bunch stem) of Vitis vinifera L. cv. Shiraz scions over own-rooted Shiraz vines at harvest. IBMP extracted from rachis during red wine fermentation can contribute potent "green" flavors. Methoxypyrazines (MPs) are normally present in Cabernet Sauvignon grapes, rachis, and wines, but it is unknown whether rootstocks can influence the MP concentration in the rachis. This study considered the effect of eight rootstocks including Ramsey and own roots on the concentrations of IBMP, 3-isopropyl-2-methoxypyrazine (IPMP), and 3-sec-butyl-2-methoxypyrazine (SBMP) in the rachis and grapes of Cabernet Sauvignon over two seasons. IBMP predominated, and its concentration in rachis and berries at harvest was significantly affected by rootstock and growing season. In the 2020 vintage, light exclusion, vine vigor, and spatial variation in vine vigor were shown to significantly affect MP concentrations in rachis.

Modelling the antimicrobial pharmacodynamics for bacterial strains with versus without acquired resistance to fluoroquinolones or cephalosporins.

OBJECTIVES: Antimicrobial resistance threatens therapeutic options for human and animal bacterial diseases worldwide. Current antimicrobial treatment regimens were designed against bacterial strains that were fully susceptible to them. To expand the useable lifetime of existing antimicrobial drug classes by modifying treatment regimens, data are needed on the antimicrobial pharmacodynamics (PD) against strains with reduced susceptibility. In this study, we generated and mathematically modelled the PD of the fluoroquinolone ciprofloxacin and the cephalosporin ceftriaxone against non-typhoidal Salmonella enterica subsp. enterica strains with varying levels of acquired resistance. METHODS: We included Salmonella strains across categories of reduced susceptibility to fluoroquinolones or cephalosporins reported to date, including isolates from human infections, food-animal products sold in retail, and food-animal production. We generated PD data for each drug and strain via time-kill assay. Mathematical models were compared in their fit to represent the PD. The best-fit model's parameter values across the strain susceptibility categories were compared. RESULTS: The inhibitory baseline sigmoid Imax (or Emax) model was best fit for the PD of each antimicrobial against a majority of the strains. There were statistically significant differences in the PD parameter values across the strain susceptibility categories for each antimicrobial. CONCLUSION: The results demonstrate predictable multiparameter changes in the PD of these first-line antimicrobials depending on the Salmonella strain's susceptibility phenotype and specific genes conferring reduced susceptibility. The generated PD parameter estimates could be used to optimise treatment regimens against infections by strains with reduced susceptibility.

Scientific foundations for an ecosystem goal, milestones and indicators for the post-2020 global biodiversity framework.

Despite substantial conservation efforts, the loss of ecosystems continues globally, along with related declines in species and nature's contributions to people. An effective ecosystem goal, supported by clear milestones, targets and indicators, is urgently needed for the post-2020 global biodiversity framework and beyond to support biodiversity conservation, the UN Sustainable Development Goals and efforts to abate climate change. Here, we describe the scientific foundations for an ecosystem goal and milestones, founded on a theory of change, and review available indicators to measure progress. An ecosystem goal should include three core components: area, integrity and risk of collapse. Targets-the actions that are necessary for the goals to be met-should address the pathways to ecosystem loss and recovery, including safeguarding remnants of threatened ecosystems, restoring their area and integrity to reduce risk of collapse and retaining intact areas. Multiple indicators are needed to capture the different dimensions of ecosystem area, integrity and risk of collapse across all ecosystem types, and should be selected for their fitness for purpose and relevance to goal components. Science-based goals, supported by well-formulated action targets and fit-for-purpose indicators, will provide the best foundation for reversing biodiversity loss and sustaining human well-being.

Estimating blue carbon sequestration under coastal management scenarios.

Restoring and protecting "blue carbon" ecosystems - mangrove forests, tidal marshes, and seagrass meadows - are actions considered for increasing global carbon sequestration. To improve understanding of which management actions produce the greatest gains in sequestration, we used a spatially explicit model to compare carbon sequestration and its economic value over a broad spatial scale (2500 km of coastline in southeastern Australia) for four management scenarios: (1) Managed Retreat, (2) Managed Retreat Plus Levee Removal, (3) Erosion of High Risk Areas, (4) Erosion of Moderate to High Risk Areas. We found that carbon sequestration from avoiding erosion-related emissions (abatement) would far exceed sequestration from coastal restoration. If erosion were limited only to the areas with highest erosion risk, sequestration in the non-eroded area exceeded emissions by 4.2 million Mg CO2 by 2100. However, losing blue carbon ecosystems in both moderate and high erosion risk areas would result in net emissions of 23.0 million Mg CO2 by 2100. The removal of levees combined with managed retreat was the strategy that sequestered the most carbon. Across all time points, removal of levees increased sequestration by only an additional 1 to 3% compared to managed retreat alone. Compared to the baseline erosion scenario, the managed retreat scenario increased sequestration by 7.40 million Mg CO2 by 2030, 8.69 million Mg CO2 by 2050, and 16.6 million Mg CO2 by 2100. Associated economic value followed the same patterns, with large potential value loss from erosion greater than potential gains from conserving or restoring ecosystems. This study quantifies the potential benefits of managed retreat and preventing erosion in existing blue carbon ecosystems to help meet climate change mitigation goals by reducing carbon emissions.

Combating ecosystem collapse from the tropics to the Antarctic.

Globally, collapse of ecosystems-potentially irreversible change to ecosystem structure, composition and function-imperils biodiversity, human health and well-being. We examine the current state and recent trajectories of 19 ecosystems, spanning 58° of latitude across 7.7 M km2 , from Australia's coral reefs to terrestrial Antarctica. Pressures from global climate change and regional human impacts, occurring as chronic 'presses' and/or acute 'pulses', drive ecosystem collapse. Ecosystem responses to 5-17 pressures were categorised as four collapse profiles-abrupt, smooth, stepped and fluctuating. The manifestation of widespread ecosystem collapse is a stark warning of the necessity to take action. We present a three-step assessment and management framework (3As Pathway Awareness, Anticipation and Action) to aid strategic and effective mitigation to alleviate further degradation to help secure our future.

A guide to representing variability and uncertainty in biodiversity indicators.

Biodiversity indicators are used to inform decisions and measure progress toward global targets, such as the United Nations Sustainable Development Goals. Indicators aggregate and simplify complex information, so underlying information influencing its reliability and interpretation (e.g., variability in data and uncertainty in indicator values) can be lost. Communicating uncertainty is necessary to ensure robust decisions and limit misinterpretations of trends, yet variability and uncertainty are rarely quantified in biodiversity indicators. We developed a guide to representing uncertainty and variability in biodiversity indicators. We considered the key purposes of biodiversity indicators and commonly used methods for representing uncertainty (standard error, bootstrap resampling, and jackknife resampling) and variability (quantiles, standard deviation, median absolute deviation, and mean absolute deviation) with intervals. Using 3 high-profile biodiversity indicators (Red List Index, Living Planet Index, and Ocean Health Index), we tested the use, suitability, and interpretation of each interval method based on the formulation and data types underpinning the indicators. The methods revealed vastly different information; indicator formula and data distribution affected the suitability of each interval method. Because the data underpinning each indicator were not normally distributed, methods relying on normality or symmetrical spread were unsuitable. Quantiles, bootstrapping, and jackknifing provided useful information about the underlying variability and uncertainty. We built a decision tree to inform selection of the appropriate interval method to represent uncertainty or variation in biodiversity indicators, depending on data type and objectives. Our guide supports transparent and effective communication of biodiversity indicator trends to facilitate accurate interpretation by decision makers.

Los indicadores de biodiversidad se usan para orientar las decisiones y medir el progreso hacia los objetivos globales, como los Objetivos de Desarrollo Sustentable de las Naciones Unidas. Los indicadores agregan y simplifican la información compleja, por lo que la información subyacente que influye sobre su confiabilidad e interpretación (p. ej.: variabilidad en los datos e incertidumbre en los valores indicadores) puede perderse. Es necesario comunicar la incertidumbre para asegurar decisiones sólidas y limitar las malas interpretaciones de las tendencias. Aun así, rara vez se cuantifican la variabilidad y la incertidumbre en los indicadores de biodiversidad. Desarrollamos una guía para representar la incertidumbre y la variabilidad en los indicadores de biodiversidad. Consideramos los propósitos importantes de los indicadores de biodiversidad y los métodos comúnmente usados para representar la incertidumbre (error estándar, remuestreo bootstrap, remuestreo jackknife) y la variabilidad (quantiles, desviación estándar, desviación mediana absoluta, desviación media absoluta) con intervalos. Usamos tres indicadores de biodiversidad de alto perfil (Red List Index, Living Planet Index, Ocean Health Index) para analizar el uso, idoneidad e interpretación de cada método de intervalo con base en la formulación y los tipos de datos fundamentales para los indicadores. Los métodos revelaron información ampliamente diferente; la fórmula del indicador y la distribución de los datos afectaron la idoneidad de cada método de intervalo. Ya que los datos fundamentales para cada indicador no tuvieron una distribución normal, los métodos que dependen de la normalidad o el esparcimiento simétrico no fueron idóneos. Los quantiles, el bootstrap y el jackknife proporcionaron información útil sobre la variabilidad y la incertidumbre subyacentes. Construimos un árbol de decisiones para guiar la selección del método de intervalo apropiado para representar la incertidumbre o la variación en los indicadores de biodiversidad, dependiendo del tipo de datos y de los objetivos. Nuestra guía respalda la comunicación efectiva y transparente de las tendencias en los indicadores de biodiversidad para facilitarle al órgano decisorio la interpretación acertada de estas tendencias.

Estimating changes and trends in ecosystem extent with dense time-series satellite remote sensing.

Quantifying trends in ecosystem extent is essential to understanding the status of ecosystems. Estimates of ecosystem loss are widely used to track progress toward conservation targets, monitor deforestation, and identify ecosystems undergoing rapid change. Satellite remote sensing has become an important source of information for estimating these variables. Despite regular acquisition of satellite data, many studies of change in ecosystem extent use only static snapshots, which ignores considerable amounts of data. This approach limits the ability to explicitly estimate trend uncertainty and significance. Assessing the accuracy of multiple snapshots also requires time-series reference data which is often very costly and sometimes impossible to obtain. We devised a method of estimating trends in ecosystem extent that uses all available Landsat satellite imagery. We used a dense time series of classified maps that explicitly accounted for covariates that affect extent estimates (e.g., time, cloud cover, and seasonality). We applied this approach to the Hukaung Valley Wildlife Sanctuary, Myanmar, where rapid deforestation is greatly affecting the lowland rainforest. We applied a generalized additive mixed model to estimate forest extent from more than 650 Landsat image classifications (1999-2018). Forest extent declined significantly at a rate of 0.274%/year (SE = 0.078). Forest extent declined from 91.70% (SE = 0.02) of the study area in 1999 to 86.52% (SE = 0.02) in 2018. Compared with the snapshot method, our approach improved estimated trends of ecosystem loss by allowing significance testing with confidence intervals and incorporation of nonlinear relationships. Our method can be used to identify significant trends over time, reduces the need for extensive reference data through time, and provides quantitative estimates of uncertainty.

Estimación de los Cambios y Tendencias en la Extensión de los Ecosistemas Mediante Teledetección Satelital de Series Temporales Densas Resumen Las tendencias de cuantificación de la extensión de los ecosistemas es esencial para el entendimiento de su estado. Las estimaciones de pérdidas de los ecosistemas se usan con amplitud para rastrear el progreso hacia los objetivos de conservación, monitorear la deforestación e identificar a los ecosistemas que están experimentando un cambio rápido. La teledetección satelital se ha transformado en una fuente importante de información para la estimación de estas variables. A pesar de la obtención de datos satelitales, muchos estudios sobre el cambio en la extensión de los ecosistemas usan solamente capturas estáticas, lo cual ignora cantidades considerables de datos. Esta estrategia limita la habilidad que se tiene para estimar explícitamente la incertidumbre e importancia de la tendencia. La valoración de la precisión de múltiples capturas también requiere datos de referencia de series temporales, lo cual es muy costoso e imposible de conseguir en algunos casos. Diseñamos un método para estimar las tendencias en la extensión de los ecosistemas que usa todas las imágenes satelitales disponibles en Landsat. Usamos una serie temporal densa de los mapas clasificados que considera explícitamente a las covarianzas que afectan a las estimaciones de la extensión (p.ej.: tiempo, cobertura de nubes y estacionalidad). Aplicamos esta estrategia en el Santuario de Vida Silvestre del Valle de Huakaung en Myanmar, en donde la deforestación acelerada está afectando enormemente a la selva de tierras bajas. Aplicamos también un modelo mixto, aditivo y generalizado para estimar la extensión del bosque a partir de más de 650 clasificaciones de imágenes en Landsat (1999 - 2018). La extensión del bosque declinó significativamente a una tasa de 0.274%/año (SE 0.078). La extensión del bosque declinó del 91.70% (SE 0.02) del área de estudio en 1999 a 86.52% (SE 0.02) en 2018. Si la comparamos con la estrategia de las capturas, nuestra estrategia mejoró las tendencias estimadas de la pérdida del ecosistema al permitir la evaluación de significancia con intervalos de confianza y la incorporación de relaciones no lineales. Nuestro método puede usarse para identificar las tendencias significativas a lo largo del tiempo; también reduce la necesidad de tener datos de referencia extensos a lo largo del tiempo y proporciona estimaciones cuantitativas de la incertidumbre.

Matching biodiversity indicators to policy needs.

At the global scale, biodiversity indicators are typically used to monitor general trends, but are rarely implemented with specific purpose or linked directly to decision making. Some indicators are better suited to predicting future change, others are more appropriate for evaluating past actions, but this is seldom made explicit. We developed a conceptual model for assigning biodiversity indicators to appropriate functions based on a common approach used in economics. Using the model, indicators can be classified as leading (indicators that change before the subject of interest, informing preventative actions), coincident (indicators that measure the subject of interest), or lagging (indicators that change after the subject of interest has changed and thus can be used to evaluate past actions). We classified indicators based on ecological theory on biodiversity response times and management objectives in 2 case studies: global species extinction and marine ecosystem collapse. For global species extinctions, indicators of abundance (e.g., the Living Planet Index or biodiversity intactness index) were most likely to respond first, as leading indicators that inform preventative action, while extinction indicators were expected to respond slowly, acting as lagging indicators flagging the need for evaluation. For marine ecosystem collapse, indicators of direct responses to fishing were expected to be leading, while those measuring ecosystem collapse could be lagging. Classification defines an active role for indicators within the policy cycle, creates an explicit link to preventative decision-making, and supports preventative action.

Alineamiento entre los Indicadores de Biodiversidad y los Requerimientos Políticos Resumen En la escala global, los indicadores de biodiversidad se usan comúnmente para monitorear las tendencias generales pero rara vez se implementan con un propósito específico o vinculados directamente con la toma de decisiones. Algunos indicadores son mejores para predecir los cambios futuros, mientras que otros son más apropiados para la evaluación de acciones pasadas, aunque lo anterior casi nunca se comunica explícitamente. Desarrollamos un modelo conceptual para la atribución de indicadores de biodiversidad a funciones apropiadas con base en una estrategia común que se usa en la economía. Con este modelo, los indicadores pueden clasificarse como principales (indicadores que cambian antes que el sujeto de interés, orientando así las acciones preventivas), coincidentes (indicadores que miden al sujeto de interés) o rezagados (indicadores que cambian después de que el sujeto de interés ha cambiado y por lo tanto puede usarse para evaluar las acciones pasadas). Clasificamos los indicadores con base en la teoría ecológica sobre los tiempos de respuesta de la biodiversidad y los objetivos de manejo en dos estudios de caso: la extinción mundial de especies y el colapso de los ecosistemas marinos. Para la extinción mundial de especies, los indicadores de abundancia (p. ej.: el Índice del Planeta Viviente o el índice de biodiversidad intacta) fueron los más probables en tener una respuesta pronta como indicadores principales que orientan las acciones preventivas, mientras que se esperó que los indicadores de extinción tuvieran respuestas lentas, por lo que actuarían como indicadores rezagados que disminuyeron la necesidad de evaluación. Para el colapso de los ecosistemas marinos, se anticipó que los indicadores de las respuestas directas a la pesca fueran los indicadores principales, mientras que aquellos que miden el colapso del ecosistema podrían ser indicadores rezagados. La clasificación define un papel activo para los indicadores dentro del ciclo de políticas, crea un vínculo explícito con la toma de decisiones preventivas y respalda la acción preventiva.

Using decision science to evaluate global biodiversity indices.

Global biodiversity indices are used to measure environmental change and progress toward conservation goals, yet few indices have been evaluated comprehensively for their capacity to detect trends of interest, such as declines in threatened species or ecosystem function. Using a structured approach based on decision science, we qualitatively evaluated 9 indices commonly used to track biodiversity at global and regional scales against 5 criteria relating to objectives, design, behavior, incorporation of uncertainty, and constraints (e.g., costs and data availability). Evaluation was based on reference literature for indices available at the time of assessment. We identified 4 key gaps in indices assessed: pathways to achieving goals (means objectives) were not always clear or relevant to desired outcomes (fundamental objectives); index testing and understanding of expected behavior was often lacking; uncertainty was seldom acknowledged or accounted for; and costs of implementation were seldom considered. These gaps may render indices inadequate in certain decision-making contexts and are problematic for indices linked with biodiversity targets and sustainability goals. Ensuring that index objectives are clear and their design is underpinned by a model of relevant processes are crucial in addressing the gaps identified by our assessment. Uptake and productive use of indices will be improved if index performance is tested rigorously and assumptions and uncertainties are clearly communicated to end users. This will increase index accuracy and value in tracking biodiversity change and supporting national and global policy decisions, such as the post-2020 global biodiversity framework of the Convention on Biological Diversity.

Uso de las Ciencias de la Decisión para Evaluar los Índices Globales de Biodiversidad Resumen Los índices globales de biodiversidad se usan para medir el cambio ambiental y el avance hacia los objetivos de conservación, aunque pocos han sido evaluados completamente en cuanto a su capacidad para detectar las tendencias de interés como las declinaciones de especies amenazadas o la función del ecosistema. Evaluamos cualitativamente nueve índices de uso común para dar seguimiento a la biodiversidad a escala global y regional contra cinco criterios relacionados con los objetivos, diseño, comportamiento, incorporación de la incertidumbre y restricciones (p. ej.: costos y disponibilidad de datos) mediante una estrategia estructurada basada en las ciencias de la decisión. La evaluación se basó en la literatura de referencia para los índices disponibles al momento del análisis. Identificamos cuatro vacíos importantes en los índices estudiados: las vías para lograr los objetivos (objetivos medios) no fueron siempre claras o relevantes para los resultados deseados (objetivos fundamentales); el análisis del índice y el entendimiento del comportamiento esperado casi siempre fueron escasos; pocas veces se consideró o explicó la incertidumbre; y casi nunca se consideraron los costos de la implementación. Estos vacíos pueden hacer que los índices sean inadecuados en ciertos contextos de toma de decisiones y son problemáticos para los índices vinculados a los objetivos de biodiversidad y las metas de sustentabilidad. Es de suma importancia asegurarse que los objetivos del índice sean claros y que su diseño esté respaldado por un modelo de procesos relevantes para tratar con los vacíos identificados en nuestro estudio. La aceptación y el uso productivo de los índices mejorarán si el desempeño del índice es evaluado rigurosamente y las suposiciones e incertidumbres se les comunican claramente a los usuarios finales. Lo anterior aumentará la precisión y valor del índice en el seguimiento de los cambios de la biodiversidad y en el apoyo a las decisiones políticas nacionales y mundiales, como el marco de trabajo para la biodiversidad post-2020 establecido por la Convención sobre la Diversidad Biológica.

Using machine learning to understand the implications of meteorological conditions for fish kills.

Fish kills, often caused by low levels of dissolved oxygen (DO), involve with complex interactions and dynamics in the environment. In many places the precise cause of massive fish kills remains uncertain due to a lack of continuous water quality monitoring. In this study, we tested if meteorological conditions could act as a proxy for low levels of DO by relating readily available meteorological data to fish kills of grey mullet (Mugil cephalus) using a machine learning technique, the self-organizing map (SOM). Driven by different meteorological patterns, fish kills were classified into summer and non-summer types by the SOM. Summer fish kills were associated with extended periods of lower air pressure and higher temperature, and concentrated storm events 2-3 days before the fish kills. In contrast, non-summer fish kills followed a combination of relatively low air pressure, continuous lower wind speed, and successive storm events 5 days before the fish kills. Our findings suggest that abnormal meteorological conditions can serve as warning signals for managers to avoid fish kills by taking preventative actions. While not replacing water monitoring programs, meteorological data can support fishery management to safeguard the health of the riverine ecosystems.

Measuring impacts on species with models and metrics of varying ecological and computational complexity.

Approaches to assess the impacts of landscape disturbance scenarios on species range from metrics based on patterns of occurrence or habitat to comprehensive models that explicitly include ecological processes. The choice of metrics and models affects how impacts are interpreted and conservation decisions. We explored the impacts of 3 realistic disturbance scenarios on 4 species with different ecological and taxonomic traits. We used progressively more complex models and metrics to evaluate relative impact and rank of scenarios on the species. Models ranged from species distribution models that relied on implicit assumptions about environmental factors and species presence to highly parameterized spatially explicit population models that explicitly included ecological processes and stochasticity. Metrics performed consistently in ranking different scenarios in order of severity primarily when variation in impact was driven by habitat amount. However, they differed in rank for cases where dispersal dynamics were critical in influencing metapopulation persistence. Impacts of scenarios on species with low dispersal ability were better characterized using models that explicitly captured these processes. Metapopulation capacity provided rank orders that most consistently correlated with those from highly parameterized and data-rich models and incorporated information about dispersal with little additional computational and data cost. Our results highlight the importance of explicitly considering species' ecology, spatial configuration of habitat, and disturbance when choosing indicators of species persistence. We suggest using hybrid approaches that are a mixture of simple and complex models to improve multispecies assessments.

Medición de los Impactos sobre las Especies con Modelos y Medidas de Complejidad Ecológica y Computacional Variante Resumen Las estrategias para evaluar el impacto de los escenarios de perturbación de paisaje sobre la distribución de las especies van desde las medidas basadas en patrones de presencia o hábitat hasta los modelos integrales que incluyen explícitamente a los procesos ecológicos. La elección de medidas y modelos afecta la interpretación de los impactos y las decisiones de conservación. Exploramos los impactos de tres escenarios realistas de perturbación sobre cuatro especies con características ecológicas y taxonómicas diferentes. Usamos progresivamente modelos y medidas más complejas para evaluar el impacto relativo y la clasificación de los escenarios sobre las especies. Los modelos variaron desde aquellos de distribución de especies que dependen de las suposiciones implícitas acerca de los factores ambientales y la presencia de la especie hasta aquellos modelos poblacionales explícitos con una alta parametrización espacial que incluyen los procesos ecológicos y la estocasticidad. Las medidas tuvieron un desempeño uniforme en la clasificación de los diferentes escenarios de acuerdo a la gravedad, principalmente cuando la variación en el impacto fue causada por la cantidad de hábitat presente. Sin embargo, las medidas difirieron en la clasificación para los casos en los que las dinámicas de dispersión fueron significativas en la influencia de la persistencia metapoblacional. Los impactos de los escenarios sobre las especies con una habilidad reducida de dispersión estuvieron mejor caracterizados con el uso de modelos que capturaron explícitamente estos procesos. La capacidad metapoblacional proporcionó categorías de clasificación con la correlación más consistente a aquellas provenientes de los modelos ricos en datos y con una alta parametrización e incorporó información sobre la dispersión con un reducido costo adicional de cómputo y de datos. Nuestros resultados resaltan la importancia de la consideración explícita de la ecología de las especies, la configuración espacial del hábitat y la perturbación cuando se eligen los indicadores de la persistencia de una especie. Sugerimos que se usen estrategias híbridas que mezclen modelos simples y complejos para mejorar las evaluaciones realizadas a múltiples especies.

Quantifying welfare gains of coastal and estuarine ecosystem rehabilitation for recreational fisheries.

Coastal and estuarine ecosystems, such as mangroves, tidal marshes and seagrass meadows, provide a range of ecosystem services, but have seen extensive degradation and decline. Effective protection and rehabilitation of coastal ecosystems requires an understanding of how efforts may improve associated ecosystem services. In this study, we present a spatially-explicit angler catch function to predict boat-based recreational catch as a function of ecosystem and angler characteristics. We developed a choice model to investigate where recreational anglers launch their boats and fish in southeast Australia. By linking the recreational catch models with a choice model, we were able to quantify welfare gains of ecosystem rehabilitation. We found welfare gains across fishing locations varied widely due to heterogeneous coverage of seagrass. The welfare gains of different fishing locations ranged from near-zero in areas of low seagrass coverage, to AU $19.18 (10% increase in seagrass area) and to AU $85.55 (30% increase) per trip in location of high seagrass coverage. Given two million fishing trips occurring per year in Port Phillip Bay, and one million in Western Port, the aggregated welfare gain could scale up to AU $6.2 million with a 10% increase in seagrass coverage, and AU $22 million per annum with a 30% increase in seagrass. We also calculated the welfare loss associated with total loss of seagrass ecosystem in each fishing location to represent the current value, which varied significantly, ranging from near-zero in some locations to AU $87.47 per trip in other locations. Over the past several decades, the bay-wide seagrass ecosystem has dropped by 36.7% in Western Port, resulting in potential welfare loss of an estimated AU $ 86.7 million per annum. Our analyses provide insightful spatial policy implications for coastal and marine ecosystem rehabilitation in the region.

Investigation of intraregional variation, grape amino acids, and pre-fermentation freezing on varietal thiols and their precursors for Vitis vinifera Sauvignon blanc.

Sauvignon blanc grape samples (n = 21) from across a single Geographical Indication of South Australia were analysed for thiol precursors and amino acids, and fermented in an identical laboratory-scale fermentation trial to investigate the intraregional pattern of varietal thiols in the wines. Precursors and thiols exhibited obvious intraregional diversity, and notably, stronger correlations were observed between a number of amino acids and thiol precursors (especially with glutamic acid, r ≤ -0.73) rather than free thiols. Additionally, pre-fermentation freezing (-20 °C, 1 month) was applied to five selected fresh grape samples and their juices, followed by identical fermentation. In comparison to wines from fresh grapes or frozen juices, significant elevation of varietal thiols (up to 10-fold) occurred in the wines derived from frozen grapes, with parallel increases of precursors (up to 19-fold) in juices from frozen berries. These novel results may lead to new strategies for thiol enhancement during winemaking.

Simultaneous-count models to estimate abundance from counts of unmarked individuals with imperfect detection.

We developed a method to estimate population abundance from simultaneous counts of unmarked individuals over multiple sites. We considered that at each sampling occasion, individuals in a population could be detected at 1 of the survey sites or remain undetected and used either multinomial or binomial simultaneous-count models to estimate abundance, the latter being equivalent to an N-mixture model with one site. We tested model performance with simulations over a range of detection probabilities, population sizes, growth rates, number of years, sampling occasions, and sites. We then applied our method to 3 critically endangered vulture species in Cambodia to demonstrate the real-world applicability of the model and to provide the first abundance estimates for these species in Cambodia. Our new approach works best when existing methods are expected to perform poorly (i.e., few sites and large variation in abundance among sites) and if individuals may move among sites between sampling occasions. The approach performed better when there were >8 sampling occasions and net probability of detection was high (>0.5). We believe our approach will be useful in particular for simultaneous surveys at aggregation sites, such as roosts. The method complements existing approaches for estimating abundance of unmarked individuals and is the first method designed specifically for simultaneous counts.

Modelos de Conteo Simultáneo para Estimar la Abundancia a partir de Conteos de Individuos No Marcados con Detección Imperfecta Resumen Desarrollamos un método para estimar la abundancia poblacional a partir de conteos simultáneos de individuos sin marcaje en múltiples sitios. Consideramos que en cada ocasión de muestreo los individuos de una población podrían ser detectados en uno de los sitios de censos o podrían permanecer sin ser detectados y usamos modelos de conteo simultáneo multinomial o binomial para estimar la abundancia, con el binomial como equivalente a un modelo de mezcla N con un solo sitio. Probamos el desempeño del modelo con simulaciones en un rango de probabilidades de detección, tamaños poblacionales, tasas de crecimiento, número de años, ocasiones de muestreo, y sitios. Después aplicamos nuestro método a tres especies de buitre que se encuentran en peligro crítico en Camboya para demostrar cuán aplicable es el modelo en el mundo real y para proporcionar las primeras estimaciones de abundancia para estas especies en Camboya. Nuestra nueva estrategia trabaja de mejor manera cuando se espera que los modelos existentes tengan un desempeño pobre (es decir, pocos sitios y una gran variación en la abundancia entre sitios) y si los individuos podrían moverse de un sitio a otro entre cada ocasión de muestreo. La estrategia tuvo un mejor desempeñó cuando hubo >8 ocasiones de muestreo y la probabilidad neta de detección fue alta (>0.5). Creemos que nuestra estrategia será especialmente útil para censos simultáneos en sitios de agregación, como los nidos. El método complementa las estrategias existentes para estimar la abundancia de individuos sin marcaje y es el primer método diseñado específicamente para conteos simultáneos.

Scenarios and Models to Support Global Conservation Targets.

Global biodiversity targets have far-reaching implications for nature conservation worldwide. Scenarios and models hold unfulfilled promise for ensuring such targets are well founded and implemented; here, we review how they can and should inform the Aichi Targets of the Strategic Plan for Biodiversity and their reformulation. They offer two clear benefits: providing a scientific basis for the wording and quantitative elements of targets; and identifying synergies and trade-offs by accounting for interactions between targets and the actions needed to achieve them. The capacity of scenarios and models to address complexity makes them invaluable for developing meaningful targets and policy, and improving conservation outcomes.

Optimal soil carbon sampling designs to achieve cost-effectiveness: a case study in blue carbon ecosystems.

Researchers are increasingly studying carbon (C) storage by natural ecosystems for climate mitigation, including coastal 'blue carbon' ecosystems. Unfortunately, little guidance on how to achieve robust, cost-effective estimates of blue C stocks to inform inventories exists. We use existing data (492 cores) to develop recommendations on the sampling effort required to achieve robust estimates of blue C. Using a broad-scale, spatially explicit dataset from Victoria, Australia, we applied multiple spatial methods to provide guidelines for reducing variability in estimates of soil C stocks over large areas. With a separate dataset collected across Australia, we evaluated how many samples are needed to capture variability within soil cores and the best methods for extrapolating C to 1 m soil depth. We found that 40 core samples are optimal for capturing C variance across 1000's of kilometres but higher density sampling is required across finer scales (100-200 km). Accounting for environmental variation can further decrease required sampling. The within core analyses showed that nine samples within a core capture the majority of the variability and log-linear equations can accurately extrapolate C. These recommendations can help develop standardized methods for sampling programmes to quantify soil C stocks at national scales.