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Nitrogen (N) availability regulates the productivity of terrestrial plants and the ecological services they provide. There is evidence for both increasing and decreasing plant N availability in different biomes, but the data are fragmentary. How plant N availability responds to climate change, N deposition and increasing atmospheric CO2 concentration remains a major uncertainty in the projection of the terrestrial carbon sink. The foliar N stable isotope ratio (δ15N) is an indicator of plant N availability but its usefulness to infer long-term global patterns has been limited by data scarcity. Combining ground-based δ15N and Landsat spectra, we derived annual global maps of Landsat-based foliar δ15N as estimates of plant N availability during 1984-2022. We found significant decreases in plant N availability for 44% and increases in 16% of vegetated Earth’s surface with large spatial heterogeneity. Plant N availability declined in woody-dominated ecosystems but increased in herbaceous-dominated ones. These δ15N trends were consistently and negatively correlated with the trends of Normalised-Difference-Vegetation-Index as they varied across ecosystems, suggesting increasing plant cover could have led to decreasing plant N availability. Our results indicate possible future reductions in plant N availability in many terrestrial ecosystems and provide a useful way to monitor those changes globally.

The expert group on scenarios and models of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services initiated the development of the Nature Futures Framework for developing scenarios of positive futures for nature, to help inform assessments of policy options. This new scenarios and modelling Framework seeks to open up diversity and plurality of perspectives by differentiating three main value perspectives on nature – Nature for Nature (intrinsic values of nature), Nature for Society (instrumental values) and Nature as Culture (relational values). This paper describes how the Nature Futures Framework can be applied in modelling to support policy processes by identifying key interventions for change in realizing a diversity of desirable futures. First, the paper introduces and elaborates on key building blocks of the framework for developing qualitative scenarios and translating them into quantitative scenarios: i) multiple value perspectives on nature and the Nature Futures frontier representing diverse preferences, ii) incorporating mutual and key feedbacks of social-ecological systems in Nature Futures scenarios, and iii) indicators describing the evolution of social-ecological systems with complementary knowledge and data. This paper then presents three possible application approaches to modelling Nature Futures scenarios to support the i) review, ii) implementation and iii) design phases of policy processes. The main objective of this paper is to facilitate the integration of the relational values of nature in models, through improved indicators and other forms of evidence, and to strengthen modelled linkages across biodiversity, ecosystems, nature’s contributions to people, and quality of life to identify science- and knowledge-based interventions and to enhance ecological understanding for achieving sustainable futures. The paper aims at stimulating the development of new scenarios and models based on this new framework by a wide community of modelers, and the testing and possible further development of the framework, particularly in the context of future IPBES assessments.

Global biodiversity and ecosystem service models typically operate independently. Ecosystem service projections may therefore be overly optimistic because they do not always account for the role of biodiversity in maintaining ecological functions. We review models used in recent global model intercomparison projects and develop a novel model integration framework to more fully account for the role of biodiversity in ecosystem function, a key gap for linking biodiversity changes to ecosystem services. We propose two integration pathways. The first uses empirical data on biodiversity–ecosystem function relationships to bridge biodiversity and ecosystem function models and could currently be implemented globally for systems and taxa with sufficient data. We also propose a trait-based approach involving greater incorporation of biodiversity into ecosystem function models. Pursuing both approaches will provide greater insight into biodiversity and ecosystem services projections. Integrating biodiversity, ecosystem function, and ecosystem service modeling will enhance policy development to meet global sustainability goals.

Biodiversity projections with uncertainty estimates under different climate, land-use, and policy scenarios are essential to setting and achieving international targets to mitigate biodiversity loss. Evaluating and improving biodiversity predictions to better inform policy decisions remains a central conservation goal and challenge. A comprehensive strategy to evaluate and reduce uncertainty of model outputs against observed measurements and multiple models would help to produce more robust biodiversity predictions. We propose an approach that integrates biodiversity models and emerging remote sensing and in-situ data streams to evaluate and reduce uncertainty with the goal of improving policy-relevant biodiversity predictions. In this article, we describe a multivariate approach to directly and indirectly evaluate and constrain model uncertainty, demonstrate a proof of concept of this approach, embed the concept within the broader context of model evaluation and scenario analysis for conservation policy, and highlight lessons from other modeling communities.

Few biodiversity indicators are available that reflect the state of broad-sense biodiversity—rather than of particular taxa—at fine spatial and temporal resolution. The Biodiversity Intactness Index (BII) estimates how the average abundance of native terrestrial species in a region compares with their abundances before pronounced human impacts. BII is designed for use with data from a wide range of taxa and functional groups and for estimation at any resolution for which data on land use and related pressures are available. For each year from 2001 to 2012, we combined models of how land use and related pressures in tropical and subtropical forested biomes affect overall abundance and compositional similarity of plants, fungi, invertebrates and vertebrates, with data on anthropogenic pressures to produce annual maps of modelled BII at a spatial resolution of 30 arc seconds (roughly 1 km at the equator) across tropical and subtropical forested biomes. This is the first time temporal change in BII has been estimated across such a large region. The approach we have used to model compositional similarity uses data more efficiently than that used previously when estimating BII. Across tropical and subtropical biomes, BII fell by an average of 1.9 percentage points between 2001 and 2012, with 81 countries seeing an average reduction and 43 an average increase; the extent of primary forest fell by 3.9% over the same period. Changes are not strongly related to countries’ rates of economic growth over the same period.

Context Ensembles of artificial neural network models can be trained to predict the continuous characteristics of vegetation such as the foliage cover and species richness of different plant functional groups. Objectives Our first objective was to synthesise existing site-based observations of native plant species to quantify summed percentage foliage cover and species richness within four functional groups and in totality. Secondly, we generated spatially-explicit, continuous, landscape-scale models of these functional groups, accompanied by maps of the model residuals to show uncertainty. Methods Using a case study from New South Wales, Australia, we aggregated floristic observations from 6806 sites into four common plant growth forms (trees, shrubs, grasses and forbs) representing four different functional groups. We coupled these response data with spatially-complete surfaces describing environmental predictors and predictors that reflect landscape-scale disturbance. We predicted the distribution of foliage cover and species richness of these four plant functional groups over 1.5 million hectares. Importantly, we display spatially explicit model residuals so that end-users have a tangible and transparent means of assessing model uncertainty. Results Models of richness generally performed well (R2 0.43–0.63), whereas models of cover were more variable (R2 0.12–0.69). RMSD ranged from 1.42 (tree richness) to 29.86 (total native cover). MAE ranged from 1.0 (tree richness) to 20.73 (total native foliage cover). Conclusions Continuous maps of vegetation attributes can add considerable value to existing maps and models of discrete vegetation classes and provide ecologically informative data to support better decisions across multiple spatial scales.

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.在全球范围内, 生物多样性指标常常用于监测整体趋势, 但很少有具体实施目的或直接与决策挂钩。有些指标更适合预测未来的变化, 有些指标则更适合评价过去的行动, 但这一点很少得到说明。基于经济学中的常用方法, 我们开发了一个根据恰当功能分配生物多样性指标的概念模型。该模型将指标分为领先指标 (先于研究主体发生变化的指标, 可为预防性行动提供信息) 、同步指标 (度量研究主体的指标) 及滞后指标 (研究主体变化后才发生变化的指标, 可用于评估过去的行动) 。我们以全球物种灭绝和海洋生态系统崩溃作为案例, 根据生物多样性响应时间的生态学理论和管理目标对相应指标进行了分类。在全球物种灭绝的案例中, 种群数量指标 (如地球生命力指数或生物多样性完整性指数) 最有可能首先产生响应, 它们是可以为预防行动提供信息的领先指标, 而灭绝相关的指标预期响应缓慢, 则是表明评估需求的滞后指标。对于海洋生态系统崩溃而言, 衡量对渔业直接响应的指标应为领先指标, 而生态系统崩溃的相关指标可能为滞后指标。对指标的分类确定了其在政策周期中的积极作用, 建立了指标与预防性决策的明确联系, 并支持了预防性行动。【翻译: 胡怡思; 审校: 聂永刚】.

Understanding how biodiversity is changing over space and time is crucial for well-informed decisions that help retain Earth's biological heritage over the long term. Tracking changes in biodiversity through ecosystem accounting provides this important information in a systematic way and readily enables linking to other relevant environmental and economic data to provide an integrated perspective. We derived biodiversity accounts for the Murray-Darling Basin, Australia's largest catchment. We assessed biodiversity change from 2010 to 2015 for all vascular plants, all waterbirds, and 10 focal species. We applied a scalable habitat-based assessment approach that combined expected patterns in the distribution of biodiversity from spatial biodiversity models with a time series of spatially complete data on habitat condition derived from remote sensing. Changes in biodiversity from 2010 to 2015 varied across regions and biodiversity features. For the entire Murray-Darling Basin, the expected persistence of vascular plants increased slightly from 2010 to 2015 (from 86.8% to 87.1%), mean species richness of waterbirds decreased slightly (from 12.5 to 12.3 species), whereas for the focal species the estimated area of habitat increased for 8 species and decreased for 1 species. Regions in the north of the Murray-Darling Basin generally had decreases in biodiversity from 2010 to 2015, whereas in the south biodiversity was stable or increased. Our results demonstrate the benefits of habitat-based biodiversity assessments in providing fully scalable biodiversity accounts across different biodiversity features, consistent with the United Nations System of Environmental Economic Accounting - Ecosystem Accounting (SEEA EA) framework.Evaluación de la Biodiversidad con base en el Hábitat para la Contabilización de Ecosistemas en la Cuenca Murray-Darling Resumen El conocimiento sobre cómo está cambiando la biodiversidad en el tiempo y en el espacio es crucial para las decisiones bien informadas que ayudan a retener la herencia biológica de la Tierra a largo plazo. El seguimiento de cambios en la biodiversidad mediante la contabilidad de los ecosistemas proporciona esta información importante de manera sistémica y permite fácilmente la conexión con otros datos ambientales y económicos relevantes para proporcionar una perspectiva integrada. Derivamos la contabilidad de la biodiversidad para la Cuenca Murray-Darling, la mayor cuenca de Australia. Analizamos los cambios en la biodiversidad entre 2010 y 2015 de todas las plantas vasculares, todas las aves acuáticas y diez especies focales. Aplicamos una estrategia de evaluación basada en el hábitat que combinó los patrones esperados en la distribución de la biodiversidad a partir de modelos espaciales de la biodiversidad con una serie temporal de datos espacialmente completos derivados de la teledetección de la condición del hábitat. Los cambios en la biodiversidad entre 2010 y 2015 variaron entre las regiones y las características de la biodiversidad. Para toda la Cuenca Murray-Darling, la persistencia esperada de las plantas vasculares incrementó ligeramente durante los años de estudio (de 86.8% a 87.1%), la riqueza promedio de especies de aves acuáticas disminuyó un poco (de 12.5 a 12.3 especies), mientras que el área estimada del hábitat de las especies focales incrementó para ocho especies y disminuyó para una. Las regiones al norte de la cuenca tuvieron disminuciones generalizadas de la biodiversidad entre 2010 y 2015, mientras al sur, la biodiversidad se mantuvo estable o incrementó. Nuestros resultados demuestran los beneficios que tienen las evaluaciones de la biodiversidad basadas en el hábitat para proporcionar una contabilidad de la biodiversidad completamente escalable entre las diferentes características de la biodiversidad, acorde con la estructura del Sistema de Contabilidad Económico-Ambiental - Contabilidad de los Ecosistemas (SEEA EA) de las Naciones Unidas.了解生物多样性的时空变化对于制定帮助地球生物遗产长期续存的决策至关重要。通过生态系统核算追踪生物多样性的变化, 可以系统地提供这一重要信息, 并能随时与其他相关的环境和经济数据相联系, 提供综合性视角。本研究对澳大利亚最大的集水区--墨累达令盆地的生物多样性进行了核算, 评估了2010年到2015年所有维管植物、所有水鸟和10个重点保护物种的生物多样性变化。我们采用了一种尺度可变的基于栖息地的评估方法, 整合了空间生物多样性模型的生物多样性分布格局估计与遥感得到的栖息地状况空间完整数据的时间序列。研究发现, 2010年至2015年, 生物多样性的变化在不同地区和生物多样性特征之间有所差异。整个墨累达令流域维管植物的预期续存情况在2010年到2015年略有增加(从86.8%到87.1%), 水鸟的平均物种丰富度略有下降(从12.5种到12.3种), 而重点保护物种中8个物种的栖息地面积预计增加, 1个物种的栖息地面积下降。此外, 墨累达令盆地北部地区的生物多样性从2010年到2015年普遍下降, 而南部地区的生物多样性则保持稳定或有所增长。我们的结果表明, 基于栖息地的生物多样性评估有助于提供不同生物多样性特征全尺度的生物多样性核算, 这也符合联合国环境经济核算体系-生态系统核算(SEEA EA)框架。【翻译:胡怡思;审校:聂永刚】.

Species distribution models (SDMs) are widely used to predict and study distributions of species. Many different modeling methods and associated algorithms are used and continue to emerge. It is important to understand how different approaches perform, particularly when applied to species occurrence records that were not gathered in struc­tured surveys (e.g. opportunistic records). This need motivated a large-scale, collaborative effort, published in 2006, that aimed to create objective comparisons of algorithm performance. As a benchmark, and to facilitate future comparisons of approaches, here we publish that dataset: point location records for 226 anonymized species from six regions of the world, with accompanying predictor variables in raster (grid) and point formats. A particularly interesting characteristic of this dataset is that independent presence-absence survey data are available for evaluation alongside the presence-only species occurrence data intended for modeling. The dataset is available on Open Science Framework and as an R package and can be used as a benchmark for modeling approaches and for testing new ways to evaluate the accuracy of SDMs.

Human impacts on the Earth’s biosphere are driving the global biodiversity crisis. Governments are preparing to agree on a set of actions intended to halt the loss of biodiversity and put it on a path to recovery by 2050. We provide evidence that the proposed actions can bend the curve for biodiversity, but only if these actions are implemented urgently and in an integrated manner.

Context Management for positive biodiversity outcomes under a changing climate requires a shift of perspective relative to traditional conservation. Here we develop a repeatable indicator for measuring the capacity of landscapes to retain biodiversity under a range of plausible climate futures, as a function of the condition and spatial configuration of native habitat. Methods The Spatial Resilience Index extends an existing approach to assessing the potential for biodiversity associated with any location in a region to access suitable habitat in the surrounding landscape under climate change, incorporating multiple dispersal rates integrated over time and an optimised spatial structure. Derivation of the indicator is demonstrated for an Australian case study, covering the entire State of New South Wales, drawing on existing spatial datasets and models. Results Mapping of the Spatial Resilience Index across New South Wales suggests that different regions, and locations within these regions, vary markedly in their expected capacity to retain biodiversity, depending on the direct rate of climate change, the degree of climatic buffering (or reduction of climate velocity) afforded by landscape heterogeneity, and the degree of anthropogenic impacts on the connectedness of habitat in the landscape. The developed approach accounts for the interplay between these processes by treating them within a unified framework. Conclusions The index highlights areas which can potentially benefit from adaptive management (e.g. habitat restoration) to enhance capacity to retain biodiversity under climate change, and offers an objective means of monitoring any resulting change in this capacity over time.

Signatory countries to the Convention on Biological Diversity (CBD) are formulating goals and indicators through 2050 under the post‐2020 Global Biodiversity Framework (GBF). Among the goals is increasing the integrity of ecosystems. The CBD is now seeking input toward a quantifiable definition of integrity and methods to track it globally. Here, we offer a schema for using Earth observations (EO) to monitor and evaluate global forest ecosystem integrity (EI). Our approach builds on three topics: the concept of EI, the use of satellite‐based EO, and the use of “essential biodiversity variables” to monitor and report on it. Within this schema, EI is a measure of the structure, function, and composition of an ecosystem relative to the range of variation determined by climatic–geophysical environment. We use evaluation criteria to recommend eight potential indicators of EI that can be monitored around the globe using Earth Observations to support the efforts of nations to monitor and report progress to implement the post‐2020 GBF. If operationalized, this schema should help Parties to the CBD take action and report progress on achieving ecosystem commitments during this decade.

Given the magnitude and rate of ongoing climate change, the physiological capacity of species to tolerate extreme conditions will play a key role in influencing outcomes for biodiversity. It is also possible that species will respond to changes in climate by shifting their physiological tolerances, through genetic adaptation. How these processes influence biodiversity outcomes will be crucial in determining the most suitable management responses to retain diversity into the future. Here we assess how accounting for physiological tolerances, genetic adaptation and community assembly processes such as species replacement, influence projected climate change outcomes for the flora of Tasmania (all 2051 plant species). We incorporate these processes into the M‐SET metacommunity model and compare four different assumptions of species niches: realized niches, broader physiological tolerances and low or high capacity for genetic adaptation. Accounting for physiological tolerances rather than realized niches had the largest impact on projected outcomes, with 358 fewer species extinctions in the hottest climate scenario (mean = 30 extinctions). In contrast, adding the capacity for species physiological tolerances to shift through genetic adaptation resulted in little additional benefits for biodiversity outcomes, even under an optimistic level of adaptive capacity. We find that this is due largely to community assembly processes such as species replacement restricting the ability of species to persist and adapt in situ, as has been suggested from theoretical metacommunity models applied in simple artificial settings. Our results highlight the importance of accounting for species physiological tolerances and community‐level processes in biodiversity projections, while the potential role for genetic adaptation may be small, requiring further exploration in alternative contexts.

Aim: Observed, realized niche space often underestimates species’ physiological tolerances due to interactions with other species, dispersal constraints, and because some combinations of influential environmental factors do not currently exist in the real world. Conversely, correlative ecological niche models rely on the assumption that the range of environmental conditions encompassed by a species’ geographic distribution accurately reflects their environmental tolerances, including community-level approaches like generalized dissimilarity modelling (GDM). We extend GDM to better understand what effect broader environmental tolerances could have on compositional turnover under climate change. Innovation: We show how GDM can be adjusted as a function of best-available estimates of the average ratio between realized and potential niche widths to modify projected temporal turnover. We demonstrate this approach by using the estimated niche ratios of Australian plant species (n = 7,184) relative to thermal extremes, and the rate at which this ratio varied with temperature. The modified GDMs showed existing thermal tolerance could reduce the turnover predicted by standard models under climate change by up to 11%. We further show how the reduction in expected turnover by 2090 will influence where a greater proportion of the current community will persist in a region. Main conclusions: We suggest that standard spatial GDMs and their modified versions represent the extremes of ecological niche perspectives (i.e., realized and potential) and the range of tolerance communities may have when responding to environmental change. GDM projections therefore identify the range of uncertainty associated with a critical model assumption, and as climate change continues, ongoing community monitoring could be used to validate the balance between the two possibilities.

Measuring the status and trends of biodiversity is critical for making informed decisions about the conservation, management or restoration of species, habitats and ecosystems. Defining the reference state against which status and change are measured is essential. Typically, reference states describe historical conditions, yet historical conditions are challenging to quantify, may be difficult to falsify, and may no longer be an attainable target in a contemporary ecosystem. We have constructed a conceptual framework to help inform thinking and discussion around the philosophical underpinnings of reference states and guide their application. We characterize currently recognized historical reference states and describe them as Pre‐Human, Indigenous Cultural, Pre‐Intensification and Hybrid‐Historical. We extend the conceptual framework to include contemporary reference states as an alternative theoretical perspective. The contemporary reference state framework is a major conceptual shift that focuses on current ecological patterns and identifies areas with higher biodiversity values relative to other locations within the same ecosystem, regardless of the disturbance history. We acknowledge that past processes play an essential role in driving contemporary patterns of diversity. The specific context for which we design the contemporary conceptual frame is underpinned by an overarching goal—to maximize biodiversity conservation and restoration outcomes in existing ecosystems. The contemporary reference state framework can account for the inherent differences in the diversity of biodiversity values (e.g. native species richness, habitat complexity) across spatial scales, communities and ecosystems. In contrast to historical reference states, contemporary references states are measurable and falsifiable. This ‘road map of reference states’ offers perspective needed to define and assess the status and trends in biodiversity and habitats. We demonstrate the contemporary reference state concept with an example from south‐eastern Australia. Our framework provides a tractable way for policy‐makers and practitioners to navigate biodiversity assessments to maximize conservation and restoration outcomes in contemporary ecosystems., We synthesise currently recognised historical reference states and describe them as Pre‐Human, Indigenous Cultural, Pre‐Intensification and Hybrid‐Historical. We extend the conceptual framework to include contemporary reference states as an alternative theoretical perspective. The specific context for which we design the contemporary conceptual frame is underpinned by an overarching goal—to maximise biodiversity conservation and restoration outcomes in existing ecosystems. In contrast to historical reference states, contemporary references states are measurable and falsifiable. This ‘road map of reference states’ offers perspective needed to define and assess the status and trends in biodiversity and habitats.

Degradation and loss of natural habitat is the major driver of the current global biodiversity crisis. Most habitat conservation efforts to date have targeted small areas of highly threatened habitat, but emerging debate suggests retaining large intact natural systems may be just as important. We reconcile these perspectives by integrating fine-resolution global data on habitat condition and species assemblage turnover, to identify Earth’s high-value biodiversity habitat. These are areas in better condition than most other locations once supporting a similar assemblage of species, and are found within both intact regions and human dominated landscapes. However, only 18.6 % of this high-value habitat is currently protected globally. Averting permanent biodiversity loss requires clear spatially explicit targets for retaining these unprotected high-value habitats.

Despite the scientific consensus on the extinction crisis and its anthropogenic origin, the quantification of historical trends and of future scenarios of biodiversity and ecosystem services has been limited, due to the lack of inter-model comparisons and harmonized scenarios. Here, we present a multi-model analysis to assess the impacts of land-use and climate change from 1900 to 2050. During the 20th century provisioning services increased, but biodiversity and regulating services decreased. Similar trade-offs are projected for the coming decades, but they may be attenuated in a sustainability scenario. Future biodiversity loss from land-use change is projected to keep up with historical rates or reduce slightly, whereas losses due to climate change are projected to increase greatly. Renewed efforts are needed by governments to meet the 2050 vision of the Convention on Biological Diversity.One Sentence SummaryDevelopment pathways exist that allow for a reduction of the rates of biodiversity loss from land-use change and improvement in regulating services but climate change poses an increasing challenge.

Extensive anthropogenic activities driven by the demand for agriculture and forestry products have led to dramatic reductions in biodiversity worldwide and significant changes in the provisioning of ecosystem services. These trends are expected to continue in the future as the world continues to develop without much consideration of the role that nature plays in sustaining human livelihoods. Scenarios and models can be important tools to help policy- and decision-makers foresee the impact of their decisions; thus, increasing capacity in creating such models and scenarios is of utmost importance. However, postgraduate training schools that focus on this topic are still rare. Here we present and reflect on the experience of the São Paulo School of Advanced Science on Scenarios and Modelling on Biodiversity and Ecosystem Services to Support Human Well-Being (SPSAS Scenarios). In addition, we introduce the Special Issue of Biota Neotropica that resulted from the activities taking place during the SPSAS Scenarios. In total, nine case studies emerged from the activities carried out during SPSAS Scenarios. These focused on a variety of ecosystems, their current drivers of change and expected trends, as well as on the development of alternative positive scenarios applying the recently developed Nature Futures Framework. We emphasize the need to increase capacity in scenario and modelling skills in order to address some of the existing gaps in producing policy-relevant scenarios and models for biodiversity and ecosystem services.

Scenario-based modelling is a powerful tool to describe relationships between plausible trajectories of drivers, possible policy interventions, and impacts on biodiversity and ecosystem services. Model inter-comparisons are key in quantifying uncertainties and identifying avenues for model improvement but have been missing among the global biodiversity and ecosystem services modelling communities. The biodiversity and ecosystem services scenario-based inter-model comparison (BES-SIM) aims to fill this gap. We used global land-use and climate projections to simulate possible future impacts on terrestrial biodiversity and ecosystem services using a variety of models and a range of harmonized metrics. The goal of this paper is to reflect on the steps taken in BES-SIM, identify remaining methodological challenges, and suggest pathways for improvement. We identified five major groups of challenges; the need to: 1) better account for the role of nature in future human development storylines; 2) improve the representation of drivers in the scenarios by increasing the resolution (temporal, spatial and thematic) of land-use as key driver of biodiversity change and including additional relevant drivers; 3) explicitly integrate species- and trait-level biodiversity in ecosystem services models; 4) expand the coverage of the multiple dimensions of biodiversity and ecosystem services; and finally, 5) incorporate time-series or one-off historical data in the calibration and validation of biodiversity and ecosystem services models. Addressing these challenges would allow the development of more integrated global projections of biodiversity and ecosystem services, thereby improving their policy relevance in supporting the interlinked international conservation and sustainable development agendas.

The United Nations (UN) launched the 2030 Agenda for Sustainable Development to address an ongoing crisis: human pressure leading to unprecedented environmental degradation, climatic change, social inequality, and other negative planet-wide consequences. This crisis stems from a dramatic increase in human appropriation of natural resources to keep pace with rapid population growth, dietary shifts toward higher consumption of animal products, and higher demand for energy (1, 2). There is an increased recognition that Sustainable Development Goals (SDGs) are linked to one another (3, 4), and priorities such as food production, biodiversity conservation, and climate change mitigation cannot be considered in isolation (5⇓⇓–8). Hence, understanding those dynamics is central to achieving the vision of the UN 2030 Agenda. Infectious zoonotic diseases typically emerge as a result of complex interactions between humans and wild and/or domestic animals. Image credit: Pixabay/sasint. But environmental change also has direct human health outcomes via infectious disease emergence, and this link is not customarily integrated into planning for sustainable development. Currently, 65 countries are engaged in the Global Health Security Agenda (GHSA) and are finalizing a strategic plan for the next five years (the GHSA 2024 Roadmap) to better prevent, detect, and respond to infectious disease outbreaks in alignment with SDGs 2 and 3 on food security and human health. Without an integrated approach to mitigating the disease emergence consequences of environmental change, countries’ abilities to achieve SDGs and GHSA targets will be compromised. Emerging infectious diseases (EIDs) such as Ebola, influenza, SARS, MERS, and, most recently, coronavirus (2019-nCoV) cause large-scale mortality and morbidity, disrupt trade and travel networks, and stimulate civil unrest (9). When local emergence leads to regional outbreaks or global pandemics, the economic impacts can be devastating: The SARS outbreak in 2003, the H1N1 pandemic in 2009, and … [↵][1]1To whom correspondence may be addressed. Email: moreno.dimarco{at}uniroma1.it. [1]: #xref-corresp-1-1

Integrated high-resolution maps of carbon stocks and biodiversity that identify areas of potential co-benefits for climate change mitigation and biodiversity conservation can help facilitate the implementation of global climate and biodiversity commitments at local levels. However, the multi-dimensional nature of biodiversity presents a major challenge for understanding, mapping and communicating where and how biodiversity benefits coincide with climate benefits. A new integrated approach to biodiversity is therefore needed. Here, we (a) present a new high-resolution map of global above- and below-ground carbon stored in biomass and soil, (b) quantify biodiversity values using two complementary indices (BIp and BIr) representing proactive and reactive approaches to conservation, and (c) examine patterns of carbon–biodiversity overlap by identifying 'hotspots' (20% highest values for both aspects). Our indices integrate local diversity and ecosystem intactness, as well as regional ecosystem intactness across the broader area supporting a similar natural assemblage of species to the location of interest. The western Amazon Basin, Central Africa and Southeast Asia capture the last strongholds of highest local biodiversity and ecosystem intactness worldwide, while the last refuges for unique biological communities whose habitats have been greatly reduced are mostly found in the tropical Andes and central Sundaland. There is 38 and 5% overlap in carbon and biodiversity hotspots, for proactive and reactive conservation, respectively. Alarmingly, only around 12 and 21% of these proactive and reactive hotspot areas, respectively, are formally protected. This highlights that a coupled approach is urgently needed to help achieve both climate and biodiversity global targets. This would involve (1) restoring and conserving unprotected, degraded ecosystems, particularly in the Neotropics and Indomalaya, and (2) retaining the remaining strongholds of intactness.This article is part of the theme issue ‘Climate change and ecosystems: threats, opportunities and solutions’.

Essential biodiversity variables (EBVs) are designed to support the detection and quantification of biodiversity change and to define priorities in biodiversity monitoring. Unlike most primary observations of biodiversity phenomena, EBV products should provide information readily available to produce policy-relevant biodiversity indicators, ideally at multiple spatial scales, from global to subnational. This information is typically complex to produce from a single set of data or type of observation, thus requiring approaches that integrate multiple sources of in situ and remote sensing (RS) data. Here we present an up-to-date EBV concept for biodiversity data integration and discuss the critical components of workflows for EBV production. We argue that open and reproducible workflows for data integration are critical to ensure traceability and reproducibility so that each EBV endures and can be updated as novel biodiversity models are adopted, new observation systems become available, and new data sets are incorporated. Fulfilling the EBV vision requires strengthening efforts to mobilize massive amounts of in situ biodiversity data that are not yet publicly available and taking full advantage of emerging RS technologies, novel biodiversity models, and informatics infrastructures, in alignment with the development of a globally coordinated system for biodiversity monitoring.

Increased efforts are required to prevent further losses to terrestrial biodiversity and the ecosystem services that it provides1,2. Ambitious targets have been proposed, such as reversing the declining trends in biodiversity3; however, just feeding the growing human population will make this a challenge4. Here we use an ensemble of land-use and biodiversity models to assess whether—and how—humanity can reverse the declines in terrestrial biodiversity caused by habitat conversion, which is a major threat to biodiversity5. We show that immediate efforts, consistent with the broader sustainability agenda but of unprecedented ambition and coordination, could enable the provision of food for the growing human population while reversing the global terrestrial biodiversity trends caused by habitat conversion. If we decide to increase the extent of land under conservation management, restore degraded land and generalize landscape-level conservation planning, biodiversity trends from habitat conversion could become positive by the mid-twenty-first century on average across models (confidence interval, 2042–2061), but this was not the case for all models. Food prices could increase and, on average across models, almost half (confidence interval, 34–50%) of the future biodiversity losses could not be avoided. However, additionally tackling the drivers of land-use change could avoid conflict with affordable food provision and reduces the environmental effects of the food-provision system. Through further sustainable intensification and trade, reduced food waste and more plant-based human diets, more than two thirds of future biodiversity losses are avoided and the biodiversity trends from habitat conversion are reversed by 2050 for almost all of the models. Although limiting further loss will remain challenging in several biodiversity-rich regions, and other threats—such as climate change—must be addressed to truly reverse the declines in biodiversity, our results show that ambitious conservation efforts and food system transformation are central to an effective post-2020 biodiversity strategy. To promote the recovery of the currently declining global trends in terrestrial biodiversity, increases in both the extent of land under conservation management and the sustainability of the global food system from farm to fork are required.

AIM: Spatial modelling can provide new understanding of patterns in biodiversity and help inform policy, planning and management decisions. However, for many taxa, in many regions, issues with currently available biological data curtail the application of spatial biodiversity models. Here we develop techniques to model spatial patterns of diverse subterranean invertebrate fauna (troglofauna and stygofauna) in the Pilbara region, to better inform biodiversity planning and management for these groups. In this context, the available data are highly biased in their distribution, with specimens often identified only at coarse taxonomic resolution, and individual community surveys sampling a relatively small proportion of the total assemblage at a location. LOCATION: Pilbara bioregion, NW Australia. METHODS: To overcome the challenges presented by the subterranean fauna data sets, we developed a community‐level spatial biodiversity modelling framework, and applied this to model and project patterns in species richness and compositional turnover, for both troglofauna and stygofauna, using fine‐resolution (~30 m) environmental layers. RESULTS: While there was a large amount of inherent noise in the collated data, our spatial models detected potential environmental drivers of community diversity that align with existing knowledge. For troglofauna, species richness was associated with iron‐bearing geologies, while community compositional turnover was most strongly related to geographic distance. For stygofauna, groundwater depth was the primary predictor for richness and turnover, alongside other physicochemical groundwater attributes. We identified areas of high biodiversity significance by combining richness and turnover predictions. MAIN CONCLUSIONS: The spatial projections from our biodiversity models indicate the importance of the central Hamersley Range in harbouring unique and diverse subterranean assemblages. These models and projections provide an advanced platform for various biodiversity assessments, including identifying priorities for new surveys, and assessing the likely impacts of proposed development activities on the long‐term persistence of subterranean species in the Pilbara.

Understanding influences of environmental change on biodiversity requires consideration of more than just species richness. Here we present a novel framework for understanding possible changes in species' abundance structures within communities under climate change. We demonstrate this using comprehensive survey and environmental data from 1748 woody plant communities across southeast Queensland, Australia, to model rank-abundance distributions (RADs) under current and future climates. Under current conditions, the models predicted RADs consistent with the region's dominant vegetation types. We demonstrate that under a business as usual climate scenario, total abundance and richness may decline in subtropical rainforest and shrubby heath, and increase in dry sclerophyll forests. Despite these opposing trends, we predicted evenness in the distribution of abundances between species to increase in all vegetation types. By assessing the information rich, multidimensional RAD, we show that climate-driven changes to community abundance structures will likely vary depending on the current composition and environmental context.

Aim Despite recognition that realized distributions inherently underestimate species' physiological tolerances, we are yet to identify the extent of these differences within diverse taxonomic groups. The degree to which species could tolerate environmental conditions outside their observed distributions may have a significant impact on the perceived extinction risk in ecological models. More information on this potential error is required to improve our confidence in management strategies. Location Australia. Time Period 1983–2012. Major Taxa Studied Plants. Methods To quantify the scale and spatial patterns of this disparity, we estimated the existing tolerance to thermal extremes of 7,124 Australian plants, more than one-third of the native continental flora, using data from cultivated records at 128 botanical gardens and nurseries. Hierarchical Bayesian beta regression was used to assess whether factors such as realized niches, traits or phylogeny could predict the incidence or magnitude of niche truncation (underestimation of thermal tolerances), while controlling for sources of collection bias. Results Approximately half of the cultivated species analysed could tolerate temperature extremes beyond those experienced in their native range. Niche truncation was predictable from the breadth and extremes of their realized niches and by traits such as plant growth form. Phylogenetic relationships with niche truncation were weak and appeared more suited to predicting thermal tolerances directly. Main conclusions This study highlights a widespread disparity between realized and potential thermal limits that may have significant implications for species' capacity to persist in situ with a changing climate. Identifying whether thermal niche truncation is the result of biotic interactions, dispersal constraints or other environmental factors could provide significant insight into community assembly at macroecological scales. Estimating niche truncation may help to explain why certain ecological communities are more resilient to change and may potentially improve the reliability of model projections under climate change.

A critical requirement in assessing progress towards global biodiversity targets is improving our capacity to measure changes in biodiversity. Global biodiversity declined between 2000 and 2010, and there are indications that the decline was greater in freshwater than in terrestrial or marine systems. However, the data, tools and methods available during that decade were inadequate to reliably quantify this decline. Recent advances in freshwater monitoring make a global assessment now close to becoming feasible. Here we identify priorities for freshwater biodiversity assessment for 2020 and 2030, based on the Essential Biodiversity Variables (EBV) framework. We identify 22 priority activities for 2020 under three of the EBV classes (species populations, community composition, and ecosystem structure), which include: a globally systematic approach to collecting and assessing species data, collating existing and new data within global platforms, coordinated effort towards mapping wetland extent at high spatial resolution, linking in-situ data to modelling across regions, and mobilising citizen science for the collection and verification of data. Accomplishing these will allow the state of global biodiversity to be assessed according to a Red List Index with expanded geographic and taxonomic cover, an improved freshwater Living Planet Index with a greater number and phylogenetic range of species, measures of alpha and beta diversity, and globally-consistent estimates of wetland extent. To assess variables in the other EBV classes (genetic composition, species traits, and ecosystem function) we identify 15 priorities, which include development of environmental DNA methods, species-traits databases, eco-informatics and modelling over the next 15 years.

The essential biodiversity variables (EBV) framework was developed primarily to improve the detection of significant changes in global biodiversity. Its application at national level must support county-specific policy and management needs as well as allowing comparisons of estimates of biodiversity change between countries and their aggregation for reporting at regional, continental and global scales. Here we outline a process for prioritising biodiversity variables at national scale using the EBV framework. The process involves separately identifying candidate EBVs that are useful for tracking important changes in biodiversity in each major ecological feature in each ecoregion within a country. The list is then prioritised based on the proportion of ecological feature ecoregion combinations using each variable within and across terrestrial, marine and freshwater realms. We showcased this process in Australian state of New South Wales (NSW) using terrestrial, marine and freshwater ecoregions of the world as bioregional strata; vegetation formations as terrestrial ecological features, and broad aquatic ecosystem types as marine and freshwater ecological features. There was sufficient knowledge of ecological processes to identify useful variables for 85% of the ecological feature ecoregion combinations in NSW. Eleven candidate EBVs covering all six EBV classes, were useful in all three environmental realms. Our structured, stepwise approach to variable selection provides a transparent process for identifying important elements of ecological theory underpinning biodiversity monitoring within countries. Worldwide adoption of a process for prioritising biodiversity variables such as the one we propose here would help ensure consistency of national contributions to global biodiversity assessments.

Freshwater aquatic ecosystems are in decline due to intensifying land use, salinisation, water abstraction and climate change. Understanding compositional patterns in aquatic biota is a useful step towards better management of aquatic ecosystems. We used generalised dissimilarity modelling (GDM) to predict compositional turnover in riverine invertebrate fauna (primarily insects) as a function of environment. Conceptual understanding of major drivers of aquatic invertebrate species distribution helped decide which predictor variables to source and include. Five groups of environmental variables—waterscape, local habitat, climate, landscape and disturbance—were derived from either spatial layers or in situ (site) measurements. Predictive models and variance partitioning tests of variable groups demonstrated the importance of representing all conceptual drivers of ecological pattern and process. As expected, waterscape variables were independently the most important group, followed by local habitat and landscape variables; with complex interactions between groups. Climate variables independently contributed the least. To determine the information content for mapping patterns, we investigated the independent and combined contribution of site-measured and spatial predictors. Even though predictive models developed using only site-measured variables or only spatial variables explained around the same amount of deviance (DE), combined they increased explained model DE by 11.2%. Compositional dissimilarities between the 51 surveyed site pairs predicted by the model using only spatial variables were highly correlated (r2 = .85) with dissimilarities predicted using site and spatial variables. These results support the use of the spatial model for conservation decisions. The spatial model was used to evaluate representativeness of both the conservation reserve network and biological monitoring locations. The location of aquatic monitoring sites was uneven, with comprehensive coverage south and coastward, and less representative of inland environments. Proportional protection of ecological environments (scaled by riverine invertebrate taxa) was found to vary between 20% and 30%, being higher in southern parts where more land has been allocated to reserves and less in northern and inland parts. This demonstrated local progress towards achieving the Convention on Biological Diversity's Aichi Target 11 for inland waters. These results provide a focus for improving the robustness of information used in decisions affecting the conservation of aquatic biodiversity, including places to target to fill gaps in the reserve network and additional aquatic monitoring locations (supporting Convention on Biological Diversity's Aichi Target 19). The GDM-based approach to characterising ecological environments, provided a first quantitative foundation for comprehensively evaluating the conservation status of freshwater ecosystems in south-western Australia. Potential future applications include assessing the ecological implications of land use and climate change.

Targets for ecosystem restoration are usually specified in terms of the total area to be restored. A global analysis reveals that the benefits and costs of achieving such targets depend greatly on where this restoration occurs. Restoration sites that would maximize biodiversity and climate benefits.

Conservation effort needs to be integrated across whole regions, to include all landuses and tenures; and engage owners, managers and other interest groups. Where conditions are conducive, multi-stakeholder collaboration offers the advantages of averting conflict. It also offers a platform for the development of a shared vision for a region, and a common space for generating synergistic, place-specific innovations to achieve that vision. Spatial modelling assists in the process, evaluating and visualizing cumulative impacts arising from sets of landuse and management changes across a region. The Southern Mallee Guidelines was an offset scheme in the Southern Mallee region of NSW, Australia, developed to address jointly the agricultural development and biodiversity conservation concerns in the region. The scheme provided a timely example of place-specific response to tackling the complexity of land clearing in Australia, as policy makers were searching for alternatives to top-down regulation. At the close of the scheme, we applied a scenario-based, regional biodiversity evaluation tool known as Conservation Options in Regional Environments (CORE). It was developed to evaluate the cumulative impacts of development and conservation measures arising from the scheme. CORE is also useful as a learning and engagement tool. CORE combined a vegetation community-based evaluation, and a fauna species and functional fauna group evaluation based on metapopulation dynamics theory. Using this tool, we found that with some qualifications, the scheme broadly maintained overall biodiversity persistence in the region.

Author(s): McDonald-Spicer, Christiana J.; Moritz, Craig C.; Ferrier, Simon; Rosauer, Dan F. | Abstract: Stability, the continuity of environments or habitats through space and time, is a widely used concept in macroecology and biogeography and is often invoked in studies attempting to explain the uneven spatial distribution of biodiversity. Stability can be measured in various ways and at various spatiotemporal scales; however, few studies explicitly define their use of the term. This makes interpreting and comparing studies difficult. We suggest an integrated approach to defining measures of stability in macroecology and biogeography. This approach addresses five key challenges concerning the biological, environmental and spatiotemporal scales at which stability is assessed, and how the complexity of change across time and space is summarised into a metric of stability. Using this approach allows for clarity around the choice, conceptualisation, communication and comparison of measures of stability.

An important element of the Convention on Biological Diversity’s Aichi Target 15 – i.e. to enhance “ecosystem resilience … through conservation and restoration” – remains largely unaddressed by existing indicators. We here develop an indicator addressing just one of many possible dimensions of ecosystem resilience, by focusing on the capacity of ecosystems to retain biological diversity in the face of ongoing, and uncertain, climate change. The Bioclimatic Ecosystem Resilience Index (BERI) assesses the extent to which a given spatial configuration of natural habitat will promote or hinder climate-induced shifts in biological distributions. The approach uses existing global modelling of spatial turnover in species composition within three broad biological groups (plants, invertebrates and vertebrates) to scale projected changes in composition under a plausible range of climate scenarios. These projections serve as filters through which to analyse the configuration of habitat observed at a given point in time (e.g. for a particular year) – represented as a grid in which cells are scored in terms of habitat condition. BERI is then calculated, for each cell in this grid, as a function of the connectedness of that cell to areas of natural habitat in the surrounding landscape which are projected to support a similar composition of species under climate change to that currently associated with the focal cell. All analyses are performed at 30-arcsecond grid resolution (approximately 1km cells at the equator). Results can then be aggregated to report on status and trends for any desired set of reporting units – e.g. ecoregions, countries, or ecosystem types. We present example outputs for the Moist Tropical Forest Biome, based on a habitat-condition time series derived from the Global Forest Change dataset. We also describe how BERI is now being extended to cover all biomes (forest and non-forest) across the entire terrestrial surface of the planet.