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ContextConservation planning and land management are inherently spatial processes that are most effective when implemented over large areas.ObjectivesOur objectives were to (i) use existing plot data to aggregate species inventories to growth forms and derive indicators of vegetation structure and composition and ii) generate spatially-explicit, continuous, landscape scaled models of these discrete vegetation indicators, accompanied by maps of model uncertainty.MethodUsing a case study from New South Wales, Australia, we aggregated floristic observations from 7234 sites into growth forms. We trained ensembles of artificial neural networks (ANN) to predict the distribution of these indicators over a broad region covering 11.5 million hectares. Importantly, we show spatially explicit models of uncertainty so that end-users have a tangible and transparent means of assessing models.ResultsOur key findings were firstly, widely available site-based floristic records can be used to derive aggregated indicators of the structure and composition of plant growth forms. Secondly, ANNs are a powerful method to predict continuous patterns in complex, non-linear data (Pearson’s correlation coefficient 0.83 (total native vegetation cover) to 0.42 (forb cover)). Thirdly, maps of the standardised residual error give insight into model performance and provide an assessment of model uncertainty in specific locations.ConclusionsSpatially explicit, continuous representations of vegetation composition and structural complexity can add considerable value to conventional maps of vegetation extent or community type. This application has the potential to enhance the capacity for conservation planners, landscape managers and policy-makers to make informed decisions across landscape and regional scales.

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.

Systematic conservation planning and Key Biodiversity Areas (KBAs) are the two most widely used approaches for identifying important sites for biodiversity. However, there is limited advice for conservation policy makers and practitioners on when and how they should be combined. Here we provide such guidance, using insights from the recently developed Global Standard for the Identification of KBAs and the language of decision science to review and clarify their similarities and differences. We argue the two approaches are broadly similar, with both setting transparent environmental objectives and specifying actions. There is however greater contrast in the data used and actions involved, as the KBA approach uses biodiversity data alone and identifies sites for monitoring and vigilance actions at a minimum, whereas systematic conservation planning combines biodiversity and implementation-relevant data to guide management actions. This difference means there is much scope for combining approaches, so conservation planners should use KBA data in their analyses, setting context-specific targets for each KBA type, and planners and donors should use systematic conservation planning techniques when prioritizing between KBAs for management action. In doing so, they will benefit conservation policy, practice and research by building on the collaborations formed through the KBA Standard's development.

Reducing the rate of global biodiversity loss is a major challenge facing humanity1, as the consequences of biological annihilation would be irreversible for humankind2–4. Although the ongoing degradation of ecosystems5,6 and the extinction of species that comprise them7,8 are now well-documented, little is known about the role that remaining wilderness areas have in mitigating the global biodiversity crisis. Here we model the persistence probability of biodiversity, combining habitat condition with spatial variation in species composition, to show that retaining these remaining wilderness areas is essential for the international conservation agenda. Wilderness areas act as a buffer against species loss, as the extinction risk for species within wilderness communities is—on average—less than half that of species in non-wilderness communities. Although all wilderness areas have an intrinsic conservation value9,10, we identify the areas on every continent that make the highest relative contribution to the persistence of biodiversity. Alarmingly, these areas—in which habitat loss would have a more-marked effect on biodiversity—are poorly protected. Given globally high rates of wilderness loss10, these areas urgently require targeted protection to ensure the long-term persistence of biodiversity, alongside efforts to protect and restore more-degraded environments. Wilderness areas with minimal levels of human disturbance promote the persistence of biodiversity by acting as buffers against species loss, and therefore represent key targets for environmental protection.

Based on the sensitivity of species to ongoing climate change, and numerous challenges they face tracking suitable conditions, there is growing interest in species' capacity to adapt to climatic stress. Here, we develop and apply a new generic modelling approach (AdaptR) that incorporates adaptive capacity through physiological limits, phenotypic plasticity, evolutionary adaptation and dispersal into a species distribution modelling framework. Using AdaptR to predict change in the distribution of 17 species of Australian fruit flies (Drosophilidae), we show that accounting for adaptive capacity reduces projected range losses by up to 33% by 2105. We identify where local adaptation is likely to occur and apply sensitivity analyses to identify the critical factors of interest when parameters are uncertain. Our study suggests some species could be less vulnerable than previously thought, and indicates that spatiotemporal adaptive models could help improve management interventions that support increased species' resilience to climate change.

Aim Ecosystem functions such as productivity may be influenced not only by the biological diversity at each location (α-diversity) but also by the biological turnover between locations (β-diversity). We perform a continental-scale test of the strength and direction of relationships between gross primary productivity (GPP) and both α- and β-diversity. Location Continental Australia. Methods Species occurrence records were used to quantify the taxonomic α-diversity of vascular plants in approximately 11,000 1 km × 1 km grid cells across Australia, and to calculate the average β-diversity within a 10-km radius around each cell. The magnitude and variability of monthly, MODIS-derived remotely sensed GPP (2001–12) were summarized for continental Australia, as were rainfall and temperature over the same period. Generalized additive models were then used to test whether the magnitude or variability of GPP were distinctly influenced by either biodiversity measure, over and above the influence of environmental conditions. Results Precipitation and temperature explained large proportions of deviance in the magnitude (75.6%) and variability (38.3%) of GPP across the Australian continent. GPP was marginally more strongly related to species richness than it was to species turnover. However, neither diversity measure provided substantial increases in the explanatory power of GPP models over and above that of environment-only models (always

Summary Consistent and repeatable estimation of habitat condition for biodiversity assessment across large areas (i.e. regional to global) with limited field observations presents a major challenge for remote sensing (RS). RS can describe what a site looks like and how it behaves (using time series), but is unable to distinguish anthropogenic impacts from natural dynamics. Consequently, it is possible to mistake a heavily degraded habitat for a natural habitat, for example a logged forest may appear identical to an intact open woodland. This problem is compounded by the existence of multiple natural states in any given environment, and spatial variation in the natural composition and structure of vegetation as a function of variation in environment. Uncertainty in assessing habitat condition from RS is often further exacerbated by sparseness in the spatial coverage of training data. We describe a novel generic, RS-based algorithm called Habitat Condition Assessment System, designed to address the above sources of uncertainty and to be highly flexible in its application. It allows for variability in the definition of condition and in the type and quantity of input data employed. Here, we demonstrate the mechanics of the new algorithm in a simple worked example and its practical application in a case study using inferred ‘natural-only’ reference data, reflective remotely sensed data, and associated environmental data, to map condition for Australia at a 0·01° resolution. We assess the behaviour and shortcomings of the method, and compare the national case study with estimates from two existing national data sets, and field measured condition data observed at 16 967 sites across the State of Victoria. The modelled predictions outperform both of the existing national data sets, explaining 52% of the variability in field observations for well-sampled cells (relative to 8% and 12% for the existing products). The methodology can potentially address some of the key pitfalls of condition modelling and could be applied in other regions with sufficient coverage of reference data. The approach also has good potential to be extended to work with reference data for which condition is measured on a continuous scale, for example from field-based condition assessment initiatives.

Conserving different spatial and temporal dimensions of biological diversity is considered necessary for maintaining ecosystem functions under predicted global change scenarios. Recent work has shifted the focus from spatially local (α‐diversity) to macroecological scales (β‐ and γ‐diversity), emphasizing links between macroecological biodiversity and ecosystem functions (MB–EF relationships). However, before the outcomes of MB–EF analyses can be useful to real‐world decisions, empirical modeling needs to be developed for natural ecosystems, incorporating a broader range of data inputs, environmental change scenarios, underlying mechanisms, and predictions. We outline the key conceptual and technical challenges currently faced in developing such models and in testing and calibrating the relationships assumed in these models using data from real ecosystems. These challenges are explored in relation to two potential MB–EF mechanisms: “macroecological complementarity” and “spatiotemporal compensation.” Several regions have been sufficiently well studied over space and time to robustly test these mechanisms by combining cutting‐edge spatiotemporal methods with remotely sensed data, including plant community data sets in Australia, Europe, and North America. Assessing empirical MB–EF relationships at broad spatiotemporal scales will be crucial in ensuring these macroecological processes can be adequately considered in the management of biodiversity and ecosystem functions under global change.

The use of phylogenies in ecology is increasingly common and has broadened our understanding of biological diversity. Ecological sub-disciplines, particularly conservation, community ecology and macroecology, all recognize the value of evolutionary relationships but the resulting development of phylogenetic approaches has led to a proliferation of phylogenetic diversity metrics. The use of many metrics across the sub-disciplines hampers potential meta-analyses, syntheses, and generalizations of existing results. Further, there is no guide for selecting the appropriate metric for a given question, and different metrics are frequently used to address similar questions. To improve the choice, application, and interpretation of phylo-diversity metrics, we organize existing metrics by expanding on a unifying framework for phylogenetic information. Generally, questions about phylogenetic relationships within or between assemblages tend to ask three types of question: how much; how different; or how regular? We show that these questions reflect three dimensions of a phylogenetic tree: richness, divergence, and regularity. We classify 70 existing phylo-diversity metrics based on their mathematical form within these three dimensions and identify ‘anchor’ representatives: for α-diversity metrics these are PD (Faith's phylogenetic diversity), MPD (mean pairwise distance), and VPD (variation of pairwise distances). By analysing mathematical formulae and using simulations, we use this framework to identify metrics that mix dimensions, and we provide a guide to choosing and using the most appropriate metrics. We show that metric choice requires connecting the research question with the correct dimension of the framework and that there are logical approaches to selecting and interpreting metrics. The guide outlined herein will help researchers navigate the current jungle of indices.

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 1 km 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.

Global biodiversity indicators are often derived by intersecting observed or projected changes in anthropogenic pressures with underlying patterns in the distribution of biodiversity. However these patterns are typically delineated at a coarser resolution than the key ecological processes shaping both land-use and biological distributions. The ‘Biogeographic modelling Infrastructure for Large-scaled Biodiversity Indicators’ (BILBI) integrates advances in macroecological modelling, informatics, remote sensing and high-performance computing to assess spatio-temporal change in collective properties of biodiversity, particularly beta diversity, at ~1 km grid resolution across the entire terrestrial surface of the planet. BILBI greatly refines the mapping of biodiversity patterns relative to more traditional surrogates such as ecoregions and species range maps. This capability is already proving of considerable value in informing global assessments through: 1) generation of indicators of past-to-present change in biodiversity resulting from habitat transformation or protection; and 2) projection of potential future change in biodiversity resulting from alternative global-change scenarios.

1. Scientists have repeatedly argued that transformative, multiscale global scenarios are needed as tools in the quest to halt the decline of biodiversity and achieve sustainability goals. 2. As a first step towards achieving this, the researchers who participated in the scenarios and models expert group of the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services (IPBES) entered into an iterative, participatory process that led to the development of the Nature Futures Framework (NFF). 3. The NFF is a heuristic tool that captures diverse, positive relationships of humans with nature in the form of a triangle. It can be used both as a boundary object for continuously opening up more plural perspectives in the creation of desirable nature scenarios and as an actionable framework for developing consistent nature scenarios across multiple scales. 4. Here we describe the methods employed to develop the NFF and how it fits into a longer term process to create transformative, multiscale scenarios for nature. We argue that the contribution of the NFF is twofold: (a) its ability to hold a plurality of perspectives on what is desirable, which enables the development of joint goals and visions and recognizes the possible convergence and synergies of measures to achieve these visions and (b), its multiscale functionality for elaborating scenarios and models that can inform decision‐making at relevant levels, making it applicable across specific places and perspectives on nature. 5. If humanity is to achieve its goal of a more sustainable and prosperous future rooted in a flourishing nature, it is critical to open up a space for more plural perspectives of human–nature relationships. As the global community sets out to develop new goals for biodiversity, the NFF can be used as a navigation tool helping to make diverse, desirable futures possible Los científicos han argumentado repetidamente que los escenarios globales, transformadores y de escalas múltiples son necesarios como herramientas para afrontar la pérdida de biodiversidad y lograr los objetivos de sostenibilidad. Como un primer paso para lograrlo, el grupo de expertos en escenarios y modelos de la Plataforma Intergubernamental científico‐normativa en Biodiversidad y Servicios Ecosistémicos (IPBES) mediante un proceso iterativo, participativo desarrollaron el Marco de los Futuros de la Naturaleza (MFN). EL MFN es una herramienta heurística que captura las diversas relaciones positivas existentes entre los seres humanos y la naturaleza y se representa en la forma de un triángulo. Puede ser usado tanto como un objeto para generar perspectivas plurales en la creación de escenarios deseables de la naturaleza o como un marco para desarrollar escenarios de la naturaleza consistentes a través de escalas múltiples. Aquí, describimos los métodos empleados para desarrollar el MFN y como este se ajusta a un proceso de largo plazo para crear escenarios transformadores y a diferentes escalas para el futuro de la naturaleza. La contribución de los MFN persigue dos objetivos principales: a) la posibilidad de mantener una pluralidad de perspectivas en aquello que es deseable, lo que permite el desarrollo de un conjunto de objetivos y visiones reconociendo la posible convergencia y sinergias de las medidas para alcanzarlos y b) su funcionalidad a escalas múltiples para elaborar escenarios y modelos que puedan informar a los tomadores de decisiones a niveles relevantes, haciéndolo aplicable a través de perspectivas específicas sobre la naturaleza. Si la humanidad va a afrontar el objetivo de un futuro más sostenible y próspero basado en una naturaleza floreciente, es crítico dar lugar a perspectivas más plurales de las relaciones entre el ser humano y la naturaleza. Mientras la comunidad global esta desarrollando nuevos objetivos para la biodiversidad, le MFN puede ser utilizado como una herramienta de exploración que ayude a generar posibles futuros diversos y deseables para la naturaleza y las personas This work is based on the research supported in part by the National Research Foundation of South Africa (Grant Numbers 115300) and the Swedish Research Council (FORMAS Project No. 2018-02371)

AimGlobal indicators of change in the state of terrestrial biodiversity are often derived by intersecting observed or projected changes in the distribution of habitat transformation, or of protected areas, with underlying patterns in the distribution of biodiversity. However the two main sources of data used to account for biodiversity patterns in such assessments – i.e. ecoregional boundaries, and vertebrate species ranges – are typically delineated at a much coarser resolution than the spatial grain of key ecological processes shaping both land-use and biological distributions at landscape scale. Species distribution modelling provides one widely used means of refining the resolution of mapped species distributions, but is limited to a subset of species which is biased both taxonomically and geographically, with some regions of the world lacking adequate data to generate reliable models even for better-known biological groups.InnovationMacroecological modelling of collective properties of biodiversity (e.g. alpha and beta diversity) as a correlative function of environmental predictors offers an alternative, yet highly complementary, approach to refining the spatial resolution with which patterns in the distribution of biodiversity can be mapped across our planet. Here we introduce a new capability – BILBI (the Biogeographic Infrastructure for Large-scaled Biodiversity Indicators) – which has implemented this approach by integrating advances in macroecological modelling, biodiversity informatics, remote sensing and high-performance computing to assess spatial-temporal change in biodiversity at ~1km grid resolution across the entire terrestrial surface of the planet. The initial implementation of this infrastructure focuses on modelling beta-diversity patterns using a novel extension of generalised dissimilarity modelling (GDM) designed to extract maximum value from sparsely and unevenly distributed occurrence records for over 400,000 species of plants, invertebrates and vertebrates.Main conclusionsModels generated by BILBI greatly refine the mapping of beta-diversity patterns relative to more traditional biodiversity surrogates such as ecoregions. This capability is already proving of considerable value in informing global biodiversity assessment through: 1) generation of indicators of past-to-present change in biodiversity based on observed changes in habitat condition and protected-area coverage; and 2) projection of potential future change in biodiversity as a consequence of alternative scenarios of global change in drivers and policy options.

© 2017 Cambridge Philosophical Society Climate change is driving a pervasive global redistribution of the planet's species. Species redistribution poses new questions for the study of ecosystems, conservation science and human societies that require a coordinated and integrated approach. Here we review recent progress, key gaps and strategic directions in this nascent research area, emphasising emerging themes in species redistribution biology, the importance of understanding underlying drivers and the need to anticipate novel outcomes of changes in species ranges. We highlight that species redistribution has manifest implications across multiple temporal and spatial scales and from genes to ecosystems. Understanding range shifts from ecological, physiological, genetic and biogeographical perspectives is essential for informing changing paradigms in conservation science and for designing conservation strategies that incorporate changing population connectivity and advance adaptation to climate change. Species redistributions present challenges for human well-being, environmental management and sustainable development. By synthesising recent approaches, theories and tools, our review establishes an interdisciplinary foundation for the development of future research on species redistribution. Specifically, we demonstrate how ecological, conservation and social research on species redistribution can best be achieved by working across disciplinary boundaries to develop and implement solutions to climate change challenges. Future studies should therefore integrate existing and complementary scientific frameworks while incorporating social science and human-centred approaches. Finally, we emphasise that the best science will not be useful unless more scientists engage with managers, policy makers and the public to develop responsible and socially acceptable options for the global challenges arising from species redistributions.

Aim Agricultural intensification and urbanization are important drivers of biodiversity change in Europe. Different aspects of bee community diversity vary in their sensitivity to these pressures, as well as independently influencing ecosystem service provision (pollination). To obtain a more comprehensive understanding of human impacts on bee diversity across Europe, we assess multiple, complementary indices of diversity. Location One Thousand four hundred and forty six sites across Europe. Methods We collated data on bee occurrence and abundance from the published literature and supplemented them with the PREDICTS database. Using Rao's Quadratic Entropy, we assessed how species, functional and phylogenetic diversity of 1,446 bee communities respond to land‐use characteristics including land‐use class, cropland intensity, human population density and distance to roads. We combined these models with statistically downscaled estimates of land use in 2005 to estimate and map—at a scale of approximately 1 km2—the losses in diversity relative to semi‐natural/natural baseline (the predicted diversity of an uninhabited grid square, consisting only of semi‐natural/natural vegetation). Results We show that—relative to the predicted local diversity in uninhabited semi‐natural/natural habitat—half of all EU27 countries have lost over 10% of their average local species diversity and two‐thirds of countries have lost over 5% of their average local functional and phylogenetic diversity. All diversity measures were generally lower in pasture and higher‐intensity cropland than in semi‐natural/natural vegetation, but facets of diversity showed less consistent responses to human population density. These differences have led to marked spatial mismatches in losses: losses in phylogenetic diversity were in some areas almost 20 percentage points (pp.) more severe than losses in species diversity, but in other areas losses were almost 40 pp. less severe. Main conclusions These results highlight the importance of exploring multiple measures of diversity when prioritizing and evaluating conservation actions, as species‐diverse assemblages may be phylogenetically and functionally impoverished, potentially threatening pollination service provision.

The ability to monitor changes in biodiversity, and their societalimpact, is critical to conserving species and managingecosystems. While emerging technologies increase thebreadth and reach of data acquisition, monitoring efforts arestill spatially and temporally fragmented, and taxonomicallybiased. Appropriate long-term information remains thereforelimited. The Group on Earth Observations BiodiversityObservation Network (GEO BON) aims to provide a generalframework for biodiversity monitoring to support decisionmakers.Here, we discuss the coordinated observing systemadopted by GEO BON, and review challenges and advances inits implementation, focusing on two interconnected corecomponents — the Essential Biodiversity Variables as astandard framework for biodiversity monitoring, and theBiodiversity Observation Networks that support harmonizedobservation systems — while highlighting their societalrelevance.

World governments have committed to increase the global protected areas coverage by 2020, but the effectiveness of this commitment for protecting biodiversity depends on where new protected areas are located. Threshold- and complementarity-based approaches have been independently used to identify important sites for biodiversity. We brought together these approaches by performing a complementarity-based analysis of irreplaceability in important bird and biodiversity areas (IBAs), which are sites identified using a threshold-based approach. We determined whether irreplaceability values are higher inside than outside IBAs and whether any observed difference depends on known characteristics of the IBAs. We focused on 3 regions with comprehensive IBA inventories and bird distribution atlases: Australia, southern Africa, and Europe. Irreplaceability values were significantly higher inside than outside IBAs, although differences were much smaller in Europe than elsewhere. Higher irreplaceability values in IBAs were associated with the presence and number of restricted-range species; number of criteria under which the site was identified; and mean geographic range size of the species for which the site was identified (trigger species). In addition, IBAs were characterized by higher irreplaceability values when using proportional species representation targets, rather than fixed targets. There were broadly comparable results when measuring irreplaceability for trigger species and when considering all bird species, which indicates a good surrogacy effect of the former. Recently, the International Union for Conservation of Nature has convened a consultation to consolidate global standards for the identification of key biodiversity areas (KBAs), building from existing approaches such as IBAs. Our results informed this consultation, and in particular a proposed irreplaceability criterion that will allow the new KBA standard to draw on the strengths of both threshold- and complementarity-based approaches.

Aim Spatial models of the impacts of climate change generally assume that species are restricted to their realized niche, and will persist only if that niche remains accessible through time. However, species often have physiological limits beyond the range of environmental conditions experienced in their present realized niche, and these limits may also be extended further through evolution in response to the selection pressure provided by climate change. Our aim was to develop a general framework for incorporating key parameters relating to physiological limits and adaptive evolution into models of the impact of climate change. Location Global. Methods Four types of parameter are defined in our framework: the realized limit, the current physiological limit, the evolutionary physiological limit and the rate of adaptive evolution. These parameters can be estimated or predicted using a variety of information sources, and can be applied to a diverse range of modelling approaches. Results We illustrate the utility of this approach by describing how parameters can be measured directly for model species, and by exploring how minimal information on phylogeny and distribution might enable parameter estimation for less well-studied species. We outline a general strategy for deriving these parameters from ongoing research, involving a cascading hierarchy of information ranging from direct observations of traits closely linked to the parameters of interest (e.g. from physiological or evolutionary experimentation) through to more distal indicators (e.g. ecological traits such as niche position or range size). Main conclusions The incorporation of adaptive capacity into spatial modelling of biological responses to climate change is now eminently achievable. Significant sources of data are available that can be used as predictors or indicators of physiological limits and the capacity for adaptive evolution in non-model organisms. These data offer a common currency for addressing one of the most important limitations of current efforts to model the impacts of climate change on biological distributions.

Climate change is shifting the ranges of species. Simple predictive metrics of range shifts such as climate velocity, that do not require extensive knowledge or data on individual species, could help to guide conservation. We review research on climate velocity, describing the theory underpinning the concept and its assumptions. We highlight how climate velocity has already been applied in conservation-related research, including climate residence time, climate refugia, endemism, historic and projected range shifts, exposure to climate change, and climate connectivity. Finally, we discuss ways to enhance the use of climate velocity in conservation through tailoring it to be more biologically meaningful, informing design of protected areas, conserving ocean biodiversity in 3D, and informing conservation actions.

Understandably, given the fast pace of biodiversity loss, there is much interest in using Earth observation technology to track biodiversity, ecosystem functions and ecosystem services. However, because most biodiversity is invisible to Earth observation, indicators based on Earth observation could be misleading and reduce the effectiveness of nature conservation and even unintentionally decrease conservation effort. We describe an approach that combines automated recording devices, high-throughput DNA sequencing and modern ecological modelling to extract much more of the information available in Earth observation data. This approach is achievable now, offering efficient and near-real-time monitoring of management impacts on biodiversity and its functions and services., This Perspective is a product of the EO-BESS Working Group, organized by H.B., D.R. and B.C. and funded by the UK Natural Environment Research Council.

Aim: Refugia under past climates have been important in structuring current patterns in diversity, while refugia under anthropogenic climate change will likely be important in retaining this diversity and shaping new patterns. However, few studies have examined the congruence of past, present and future refugia, or the spatiotemporal connectivity of these refugia. Our aim was to test the extent of overlap of refugia under Last Glacial Maximum (LGM), present (2015) and likely future climates (2100), for Tasmania's palaeoendemic flora. We then aimed to identify areas of high spatiotemporal refugia connectivity, as priority areas for conservation and management. Location: Tasmania, Australia. Methods: We developed and applied a new community-level approach to identifying refugia, based on generalized dissimilarity modelling of compositional turnover and a set of reference sites with known biodiversity value. Using these projections of palaeoendemic plant refugia for past, present and future climates, we developed and applied a second approach to quantify the level of connectivity of these refugia over space and time. Results: Although there was large overlap (85%) between current and future climates in the distribution of the highest value palaeoendemic refugia, the small congruence of these areas with refugia at the LGM resulted in only a small area ( c. km 2 ) of persistent high value refugia over all three time periods. Despite this, our spatiotemporal analysis identified several areas of high connectivity in refugial environments for Tasmania's palaeoendemic flora over time. Main conclusions: The community-level approaches we demonstrate here to quantify refugia and their spatiotemporal connectivity have the potential to advance our understanding of biodiversity dynamics, particularly for taxonomic groups that are species-rich, poorly studied or comprised of many rare species, where species-level approaches are less suitable.

Scenarios are powerful tools toenvision how nature might respondto different pathways of futurehuman development and policy choices1.Most scenarios developed for globalenvironmental assessments have exploredimpacts of society on nature, such asbiodiversity loss, but have not includednature as a component of socioeconomicdevelopment2. They ignore policyobjectives related to nature protectionand neglect nature?s role in underpinningdevelopment and human well-being.This approach is becoming untenablebecause targets for human developmentare increasingly connected with targetsfor nature, such as in the United Nations?Sustainable Development Goals. The nextgeneration of scenarios should explorealternative pathways to reach theseintertwined targets, including potentialsynergies and trade-offs between natureconservation and other developmentgoals, as well as address feedbacks betweennature, nature?s contributions to people,and human well-being. The developmentof these scenarios would benefit from theuse of participatory approaches, integratingstakeholders from multiple sectors (forexample, fisheries, agriculture, forestry)and should address decision-makersfrom the local to the global scale3, therebysupporting assessments being undertakenby the Intergovernmental Platformon Biodiversity and EcosystemServices (IPBES). Fil: Rosa, Isabel M. D.. German Centre For Integrative Biodiversity Research; Alemania. Martin-universitat Halle-wittenberg; Alemania Fil: Pereira, Henrique M.. German Centre For Integrative Biodiversity Research; Alemania. Martin-universitat Halle-wittenberg; Alemania. Universidad de Porto; Portugal Fil: Ferrier, Simon. Csiro Land And Water; Australia Fil: Alkemade, Rob. Pbl Netherlands Environmental Assessment Agency; Países Bajos Fil: Acosta, Lilibeth A.. University Of The Philippines Los Banos; Filipinas Fil: Akcakaya, H. Resit. Stony Brook University; Estados Unidos Fil: Den Belder, Eefje. Pbl Netherlands Environmental Assessment Agency; Países Bajos. Wageningen University And Research Centre; Países Bajos Fil: Fazel, Asghar M.. Eco Institute Of Environmental Science And Technology; Irán. University Of Environment; Irán Fil: Fujimori, Shinichiro. International Institute For Applied Systems Analysis; Austria. National Institute For Environmental Studies Of Japan; Japón Fil: Harfoot, Mike. United Nations Environment Programme World Conservation Monitoring Centre; Reino Unido Fil: Harhash, Khaled A.. Egyptian Environmental Affairs Agency; Egipto Fil: Harrison, Paula A.. Lancaster University; Reino Unido Fil: Hauck, Jennifer. Coknow Consulting; Alemania. Helmholtz Centre for Environmental Research; Alemania Fil: Hendriks, Rob J. J.. Ministry Of Economic Affairs; Países Bajos Fil: Hernández, Gladys. Centre For World Economy Studies; Cuba Fil: Jetz, Walter. Imperial College London; Reino Unido. University of Yale; Estados Unidos Fil: Karlsson Vinkhuyzen, Sylvia I.. Wageningen University And Research Centre; Países Bajos Fil: Kim, Hyejin. National Institute Of Ecology; Corea del Sur Fil: King, Nicholas. North-west University; Sudáfrica Fil: Kok, Marcel T. J.. Pbl Netherlands Environmental Assessment Agency; Países Bajos Fil: Kolomytsev, Grygoriy O.. Schmalhausen Institute of Zoology of National Academy of Sciences of Ukrain; Ucrania Fil: Lazarova, Tanya. Pbl Netherlands Environmental Assessment Agency; Países Bajos Fil: Leadley, Paul. Université Paris Sud; Francia Fil: Lundquist, Carolyn J.. National Institute Of Water And Atmospheric Research; Nueva Zelanda. The University of Auckland; Nueva Zelanda Fil: García Márquez, Jaime. Humboldt-Universität zu Berlin; Alemania Fil: Meyer, Carsten. German Centre For Integrative Biodiversity Research; Alemania. University of Yale; Estados Unidos Fil: Navarro, Laetitia M.. German Centre For Integrative Biodiversity Research; Alemania. Martin-universitat Halle-wittenberg; Alemania Fil: Nesshöver, Carsten. Helmholtz Centre for Environmental Research; Alemania. German Centre for Integrative Biodiversity Research; Alemania Fil: Ngo, Hien T.. Ipbes Secretariat; Alemania Fil: Ninan, Karachepone N.. Centre For Economics, Environment And Society; India Fil: Palomo, Maria Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales “Bernardino Rivadavia”; Argentina Fil: Pereira, Laura M.. Stockholms Universitet; Suecia Fil: Peterson, Garry D.. Stockholms Universitet; Suecia Fil: Pichs, Ramon. Centre For World Economy Studies; Cuba Fil: Popp, Alexander. Potsdam Institute for Climate Impact Research; Alemania Fil: Purvis, Andy. Natural History Museum; Reino Unido Fil: Ravera, Federica. Consejo Superior de Investigaciones Científicas. Centre de Recerca Ecológica I Aplicacions Forestals; España. University Of Évora; Portugal. Universitat Central De Catalunya; España Fil: Rondinini, Carlo. Università degli studi di Roma "La Sapienza"; Italia Fil: Sathyapalan, Jyothis. Centre For Economic And Social Studies; India Fil: Schipper, Aafke M.. Pbl Netherlands Environmental Assessment Agency; Países Bajos Fil: Seppelt, Ralf. Coknow Consulting; Alemania. Martin Luther University Halle-Wittenberg; Alemania Fil: Settele, Josef. German Centre for Integrative Biodiversity Research; Alemania. Helmholtz Centre for Environmental Research; Alemania Fil: Sitas, Nadia. The Council For Scientific And Industrial Research; Sudáfrica Fil: Van Vuuren, Detlef. Pbl Netherlands Environmental Assessment Agency; Países Bajos

Distributions of Earth's species are changing at accelerating rates, increasingly driven by humanmediated climate change. Such changes are already altering the composition of ecological communities, but beyond conservation of natural systems, how and why does this matter? We review evidence that climate-driven species redistribution at regional to global scales affects ecosystem functioning, human well-being, and the dynamics of climate change itself. Production of natural resources required for food security, patterns of disease transmission, and processes of carbon sequestration are all altered by changes in species distribution. Consideration of these effects of biodiversity redistribution is critical yet lacking in most mitigation and adaptation strategies, including the United Nation's Sustainable Development Goals.

Aim Although all five of the major mediterranean-climate ecosystems (MCEs) of the world are recognized as loci of high plant species diversity and endemism, they show considerable variation in regional-scale richness. Here, we assess the role of stable Pleistocene climate and Cenozoic topography in explaining variation in regional richness of the globe's MCEs. We hypothesize that older, more climatically stable MCEs would support more species, because they have had more time for species to accumulate than MCEs that were historically subject to greater topographic upheavals and fluctuating climates. Location South-western Africa (Cape), south-western Australia, California, central Chile and the eastern (Greece) and western (Spain) Mediterranean Basin. Methods We estimated plant diversity for each MCE as the intercepts of species–area curves that are homogeneous in slope across all regions. We used two down-scaled global circulation models of the Last Glacial Maximum (LGM) to quantify climate stability by comparing the change in the location of MCEs between the LGM and present. We quantified the Cenozoic topographic stability of each MCE by comparing contemporary topographic profiles with those present in the late Oligocene and the early Pliocene. Results The most diverse MCEs – Cape and Australia – had the highest Cenozoic environmental stability, and the least diverse – Chile and California – had the lowest stability. Main conclusions Variation in plant diversity in MCEs is likely to be a consequence not of differences in diversification rates, but rather the persistence of numerous pre-Pliocene clades in the more stable MCEs. The extraordinary plant diversity of the Cape is a consequence of the combined effects of both mature and recent radiations, the latter associated with increased habitat heterogeneity produced by mild tectonic uplift in the Neogene.

Quantifying survey completeness is a key step in designing and interpreting biodiversity assessments. To date this has only been examined either at a local scale through repetitive sampling, or across broader geographic areas through multiple survey sites. In this paper, we determine the completeness of sampling at both local and continental scales, of the phytophagous arthropod assemblage on the Neotropical shrub Parkinsonia aculeata (Leguminosae). We used survey gap analysis (SGA) to determine whether existing surveys adequately sampled the diversity of environments and geographic space covered by the plant. Within defined geographic regions, we determined survey completeness at a local scale with species accumulation curves. SGA identified the highest priority sites for future sampling in the Sonoran Desert and the Pacific Coast of South America. The arthropods sampled on P. aculeata differed significantly between seasons, highlighting the importance of including surveys throughout the year. At the local scale, surveys in most regions were estimated to have sampled

Identifying refugia is a critical component of effective conservation of biodiversity under anthropogenic climate change. However, despite a surge in conceptual and practical interest, identifying refugia remains a significant challenge across diverse continental landscapes. We provide an overview of the key properties of refugia that promote species' persistence under climate change, including their capacity to (i) buffer species from climate change; (ii) sustain long-term population viability and evolutionary processes; (iii) minimize the potential for deleterious species interactions, provided that the refugia are (iv) available and accessible to species under threat. Further, we classify refugia in terms of the environmental and biotic stressors that they provide protection from (i.e. thermal, hydric, cyclonic, pyric and biotic refugia), but ideally refugia should provide protection from a multitude of stressors. Our systematic characterization of refugia facilitates the identification of refugia in the Australian landscape. Challenges remain, however, specifically with respect to how to assess the quality of refugia at the level of individual species and whole species assemblages. It is essential that these challenges are overcome before refugia can live up to their acclaim as useful targets for conservation and management in the context of climate change.

This research reports on a new approach to conservation assessment that seeks to extend the target-based model traditionally underpinning systematic conservation planning. The Biodiversity Forecasting Tool (BFT) helps answer three important questions relating to regional biodiversity persistence: ‘how much’ biodiversity can persist for a given land-management scenario; ‘what’ habitats to focus conservation effort on; and ‘where’ in the landscape to undertake conservation action. The tool integrates fine-scaled variability in vegetation composition and structure with spatial context, which is critical for ensuring the viability of populations. Thus, a raster data framework is employed which deems each location or gridcell in a landscape as contributing to biodiversity benefits to various degrees. At its simplest, just two spatial inputs, vegetation community types and vegetation condition, are needed. Drawing on, as a case-study, a broad-scale biodiversity assessment for NSW, Australia, this paper reports on the successful application of the BFT tool for a variety of functions ranging from interactive scenario evaluation through to conservation benefits mapping.

Compared to species turnover, patterns of phylogenetic turnover provide extra information about the spatial structure of biodiversity, for example providing more informative comparisons between the biota of sites which share no species. To harness this information for broad-scale spatial analysis, we present phylo-GDM, a technique for interpolating the spatial structure of phylogenetic turnover between sampled locations in relation to environment, based on generalised dissimilarity modelling (GDM). Using a database of over 150 000 location records for 114 myobatrachid frog species in Australia, linked to a specieslevel phylogeny inferred from 2467 base pairs of mitochondrial DNA, we calculated species and phylogenetic turnover between pairs of sites. We show how phylogenetic turnover extended the range of informative comparison of compositional turnover to more biologically and environmentally dissimilar sites. We generated GDM models which predict species and phylogenetic turnover across Australia, and tested the fit of models for different ages within the phylogeny to find the phylogenetic tree depth at which the relationship to current day environment is greatest. We also incorporated explanatory variables based on biogeographic patterns, to represent broad-scale turnover resulting from divergent evolutionary histories. We found that while the predictive power of our models was lower for full phylogenetic turnover than for species turnover, models based on the more recent components of the phylogeny (closer to the tips) outperformed species models and full phylogenetic models. Phylo-GDM has considerable potential as a method for incorporating phylogenetic relationships into biodiversity analyses in ways not previously possible. Because phylogenies do not require named taxa, phylo-GDM may also provide a means of including lineages with poorly resolved taxonomy (e.g. from metagenomic sequencing) into biodiversity planning and phylogeographic analysis.

“Space-for-time” substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption—that drivers of spatial gradients of species composition also drive temporal changes in diversity—rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as “time-for-time” predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

Modelling provides an effective means of integrating the complementary strengths of biodiversity data derived from in situ observation versus remote sensing. The use of modelling in biodiversity change observation, or monitoring, is just one of a number of roles that modelling can play in biodiversity assessment. These roles place different levels of emphasis on explanatory versus predictive modelling, and on modelling across space alone, versus across both space and time, either past-to-present or present-to-future. One of the most challenging, yet vitally important, applications of modelling to biodiversity monitoring involves mapping change in the distribution and retention of terrestrial biodiversity. Unlike many structural and functional attributes of ecosystems, most biological entities at the species and genetic levels of biodiversity cannot be readily detected through remote sensing. Estimating change in these levels of biodiversity across large spatial extents is therefore benefiting from advances in both species-level and community-level approaches to model-based integration of in situ biological observations and remotely sensed environmental data.

Crossing “safe” limits for biodiversity loss The planetary boundaries framework attempts to set limits for biodiversity loss within which ecological function is relatively unaffected. Newbold et al. present a quantitative global analysis of the extent to which the proposed planetary boundary has been crossed (see the Perspective by Oliver). Using over 2 million records for nearly 40,000 terrestrial species, they modeled the response of biodiversity to land use and related pressures and then estimated, at a spatial resolution of ∼1 km 2 , the extent and spatial patterns of changes in local biodiversity. Across 65% of the terrestrial surface, land use and related pressures have caused biotic intactness to decline beyond 10%, the proposed “safe” planetary boundary. Changes have been most pronounced in grassland biomes and biodiversity hotspots. Science , this issue p. 288 ; see also p. 220

Protected areas are widely considered essential for biodiversity conservation. However, few global studies have demonstrated that protection benefits a broad range of species. Here, using a new global biodiversity database with unprecedented geographic and taxonomic coverage, we compare four biodiversity measures at sites sampled in multiple land uses inside and outside protected areas. Globally, species richness is 10.6% higher and abundance 14.5% higher in samples taken inside protected areas compared with samples taken outside, but neither rarefaction-based richness nor endemicity differ significantly. Importantly, we show that the positive effects of protection are mostly attributable to differences in land use between protected and unprotected sites. Nonetheless, even within some human-dominated land uses, species richness and abundance are higher in protected sites. Our results reinforce the global importance of protected areas but suggest that protection does not consistently benefit species with small ranges or increase the variety of ecological niches., Protected areas are thought essential for biodiversity conservation, but few studies confirm that protection benefits species. Here, Gray and Hill et al. analyse a global, taxonomically broad database to show that local species richness and abundance are higher inside protected areas than outside.

Land‐use change is one of the biggest threats to biodiversity globally. The effects of land use on biodiversity manifest primarily at local scales which are not captured by the coarse spatial grain of current global land‐use mapping. Assessments of land‐use impacts on biodiversity across large spatial extents require data at a similar spatial grain to the ecological processes they are assessing. Here, we develop a method for statistically downscaling mapped land‐use data that combines generalized additive modeling and constrained optimization. This method was applied to the 0.5° Land‐use Harmonization data for the year 2005 to produce global 30″ (approx. 1 km2) estimates of five land‐use classes: primary habitat, secondary habitat, cropland, pasture, and urban. The original dataset was partitioned into 61 bio‐realms (unique combinations of biome and biogeographical realm) and downscaled using relationships with fine‐grained climate, land cover, landform, and anthropogenic influence layers. The downscaled land‐use data were validated using the PREDICTS database and the geoWiki global cropland dataset. Application of the new method to all 61 bio‐realms produced global fine‐grained layers from the 2005 time step of the Land‐use Harmonization dataset. Coarse‐scaled proportions of land use estimated from these data compared well with those estimated in the original datasets (mean R 2: 0.68 ± 0.19). Validation with the PREDICTS database showed the new downscaled land‐use layers improved discrimination of all five classes at PREDICTS sites (P

Aim Spatial turnover in plant species composition reflects the interplay between species’ environmental requirements and their dispersal dynamics. However, its origins are also historical, arising from speciation, extinction and past range dynamics. Here, we test the effects of current environmental gradients and geographical distance on regional species turnover. Using a new approach, we then interpret residual turnover patterns, unexplained by these factors. Location Native vegetation in the wheatbelt of the Southwest Australian Floristic Region (SWAFR). Methods We fitted a generalized dissimilarity model of species turnover across 650 floristic inventory plots as a function of current climate, topography, soils and inter-plot distances. We then decomposed the model errors into independent axes of residual variation, representing unexplained species turnover. We mapped the spatial patterns evident on the main residual axes, and interpreted these using ancillary data on the life form and geographical ranges of associated species, the distributions of some major habitat types, and anomalies between recent and Last Glacial Maximum (LGM) climate. Results Soil and climatic variables (especially phosphorus and wet quarter precipitation) made sizeable independent contributions to explaining deviance in species turnover, whereas deviance explained by inter-plot distances and climate co-varied strongly. Total explained deviance was 34%. Analyses of residual dissimilarity highlighted unexplained floristic similarity among many southern plots. A secondary residual axis highlighted unexplained variation in a broadly westerly to easterly direction. Consistent trends in the life form and geographical ranges of associated species suggest that these patterns are climate-related. LGM climatic anomalies were correlated with patterns on the second residual axis. Main conclusions Current soil and climatic gradients are important drivers of floristic turnover in the SWAFR. However, residual analyses revealed broad-scale gradients in floristic composition unexplained by these variables. These appear to reflect imperfect modelling of the effects of current and perhaps historical climate on species turnover.

There is increasing reliance on ecological models to improve our understanding of how ecological systems work, to project likely outcomes under alternative global change scenarios and to help develop robust management strategies. Two common types of spatiotemporally explicit ecological models are those focussed on biodiversity composition and those focussed on ecosystem function. These modelling disciplines are largely practiced separately, with separate literature, despite growing evidence that natural systems are shaped by the interaction of composition and function. Here we call for the development of new modelling approaches that integrate composition and function, accounting for the important interactions between these two dimensions, particularly under rapid global change. We examine existing modelling approaches that have begun to combine elements of composition and function, identifying their potential contribution to fully integrated modelling approaches. The development and application of integrated models of composition and function face a number of important challenges, including biological data limitations, system knowledge and computational constraints. We suggest a range of promising avenues that could help researchers overcome these challenges, including the use of virtual species, macroecological relationships and hybrid correlative-mechanistic modelling. Explicitly accounting for the interactions between composition and function within integrated modelling approaches has the potential to improve our understanding of ecological systems, provide more accurate predictions of their future states and transform their management.

Projecting the future composition and function of communities is a major challenge, and there is an urgent need to develop, improve, and test the predictive capacity of ecological models under different climate states. We tested the effect of climate on spatial patterns of plant community composition over the past 21 000 yr, focusing on whether the spatial relationships between environmental distance and compositional dissimilarity are stable over time. We used a network of fossil-pollen sites in eastern North America, combined with paleoclimate simulations from the Last Glacial Maximum (LGM; 21 000 calibrated years before present, 21 kyr BP) to the present. We modeled relationships between climate, geography, and compositional dissimilarity at 1 kyr periods using generalized dissimilarity modeling (GDM) and determined the strongest predictors of compositional dissimilarity. We assessed the performance of models calibrated for one time period (e.g. 14 kyr BP) in predicting patterns in the same period as well as at other times (e.g. 12 kyr BP), and tested whether predictive performance was related to the magnitude of climate change between the calibration and prediction time periods. Finally, we examined whether pooling data from multiple time periods improved predictive performance. Models explained 32 to 51% of compositional dissimilarity between locations within any single time period. The best set of predictors changed across time, with summer temperature and geographic distance the strongest predictors of compositional dissimilarity for most time periods. Models built for one time period explained turnover during nearby time periods relatively well, but performance decayed across time and with increasing climate change. Results were similar regardless of whether models were projected forward or backward through time, and did not improve when data were pooled across time. GDM predicts well the spatial patterns of past compositional dissimilarity and holds promise for modeling the drivers of compositional dissimilarity across space and time. However, the modeled relationships between compositional turnover and environmental distance are non-stationary, so caution is needed when predicting across periods of significant climatic change.

There is an urgent need to develop sound theory and practice for biodiversity offsets to provide a better basis for offset multipliers, to improve accounting for time delays in offset repayments, and to develop a common framework for evaluating in-kind and out-of-kind offsets. Here, we apply concepts and measures from systematic conservation planning and financial accounting to provide a basis for determining equity across type (of biodiversity), space, and time. We introduce net present biodiversity value (NPBV) as a theoretical and practical measure for defining the offset required to achieve no-net-loss. For evaluating equity in type and space we use measures of biodiversity value from systematic conservation planning. Time discount rates are used to address risk of non-repayment, and loss of utility. We illustrate these concepts and measures with two examples of biodiversity impact–offset transactions. Considerable further work is required to understand the characteristics of these approaches.

Many studies predict effects of future climate scenarios on species distributions, but few predict impacts on landscapes or ecological communities, the scales most relevant to conservation management. We combined expert knowledge with community-level spatial modelling (using artificial neural networks, ANN, and generalised dissimilarity modelling, GDM) to inform climate adaptation management in widespread but highly threatened temperate grassy ecosystems (TGE) of Australian agricultural landscapes. GDM predicted high levels of ‘biotically-scaled environmental stress’ (scaled in terms of potential change in species composition of communities) for plants, reptiles and snails within the TGE under medium, and especially high, 2070 climate scenarios. Predicted stress was lower for birds, mammals and frogs, possibly owing to generally wider species distributions, but these models do not account for changing habitat characteristics. ANN predicted environments within the current TGE biome will become increasingly favourable for formations such as chenopod shrublands, Casuarina L. forests and Callitris Vent. forests by 2070, although classification error for eucalypt woodland in current climates was high. Expert knowledge and GDM suggest these predictions may be mediated by attributes such as environmental heterogeneity that confer resilience, but GDM confirms that widespread degradation has greatly compromised the capacity of TGE to adapt to change. Based on model predictions and expert knowledge we discuss five potential climate change outcomes for TGE: decreasing fire frequency, structural change, altered functional composition, exotic invasion, and cascading changes in ecological interactions. Although significant ecological change in TGE is likely, it is feasible to ameliorate non-climatic limits to adaptation and promote reassembly by native rather than exotic species. Current conservation efforts already target similar goals, and reinforcing and adjusting these approaches offer the highest priority, lowest risk climate adaptation options. We conclude that despite high uncertainties, combining community-level modelling with expert knowledge can guide climate adaptation management.

Effective biodiversity monitoring is critical to evaluate, learn from, and ultimately improve conservation practice. Well conceived, designed and implemented monitoring of biodiversity should: (i) deliver information on trends in key aspects of biodiversity (e.g. population changes); (ii) provide early warning of problems that might otherwise be difficult or expensive to reverse; (iii) generate quantifiable evidence of conservation successes (e.g. species recovery following management) and conservation failures; (iv) highlight ways to make management more effective; and (v) provide information on return on conservation investment. The importance of effective biodiversity monitoring is widely recognized (e.g. Australian Biodiversity Strategy). Yet, while everyone thinks biodiversity monitoring is a good idea, this has not translated into a culture of sound biodiversity monitoring, or widespread use of monitoring data. We identify four barriers to more effective biodiversity monitoring in Australia. These are: (i) many conservation programmes have poorly articulated or vague objectives against which it is difficult to measure progress contributing to design and implementation problems; (ii) the case for long-term and sustained biodiversity monitoring is often poorly developed and/or articulated; (iii) there is often a lack of appropriate institutional support, co-ordination, and targeted funding for biodiversity monitoring; and (iv) there is often a lack of appropriate standards to guide monitoring activities and make data available from these programmes. To deal with these issues, we suggest that policy makers, resource managers and scientists better and more explicitly articulate the objectives of biodiversity monitoring and better demonstrate the case for greater investments in biodiversitymonitoring. There is an urgent need for improved institutional support for biodiversity monitoring in Australia, for improved monitoring standards, and for improved archiving of, and access to, monitoring data. We suggest that more strategic financial, institutional and intellectual investments in monitoring will lead to more efficient use of the resources available for biodiversity conservation and ultimately better conservation outcomes.

For many taxonomic groups, sparse information on the spatial distribution of biodiversity limits our capacity toanswer a variety of theoretical and applied ecological questions. Modelling community-level attributes (a- andb-diversity) over space can help overcome this shortfall in our knowledge, yet individually, predictions of a-or b-diversity have their limitations. In this study, we present a novel approach to combining models of a- andb-diversity, with sparse survey data, to predict the community composition for all sites in a region. We appliedour new approach to predict land snail community composition across New Zealand. As we demonstrate, thesepredictions of metacommunity composition have diverse potential applications, including predicting c-diversityfor any set of sites, identifying target areas for conservation reserves, locating priority areas for future ecologicalsurveys, generating realistic compositional data for metacommunity models and simultaneously predicting thedistribution of all species in a taxon consistent with known community diversity patterns.KeywordsAlpha, beta, community, dissimilarity, diversity, gamma, metacommunity, macroecology, richness, snail.Ecology Letters (2011) 14: 1043–1051

The geographic distributions of many taxonomic groups remain mostly unknown, hindering attempts to investigate the response of the majority of species on Earth to climate change using species distributions models (SDMs). Multi-species models can incorporate data for rare or poorly-sampled species, but their application to forecasting climate change impacts on biodiversity has been limited. Here we compare forecasts of changes in patterns of ant biodiversity in North America derived from ensembles of single-species models to those from a multi-species modeling approach, Generalized Dissimilarity Modeling (GDM). We found that both single- and multi-species models forecasted large changes in ant community composition in relatively warm environments. GDM predicted higher turnover than SDMs and across a larger contiguous area, including the southern third of North America and notably Central America, where the proportion of ants with relatively small ranges is high and where data limitations are most likely to impede the application of SDMs. Differences between approaches were also influenced by assumptions regarding dispersal, with forecasts being more similar if no-dispersal was assumed. When full-dispersal was assumed, SDMs predicted higher turnover in southern Canada than did GDM. Taken together, our results suggest that 1) warm rather than cold regions potentially could experience the greatest changes in ant fauna under climate change and that 2) multi-species models may represent an important complement to SDMs, particularly in analyses involving large numbers of rare or poorly-sampled species. Comparisons of the ability of single- and multi-species models to predict observed changes in community composition are needed in order to draw definitive conclusions regarding their application to investigating climate change impacts on biodiversity.

Aim Robust and reliable predictions of the effects of climate change on biodiversity are required in formulating conservation and management strategies that best retain biodiversity into the future. Significant challenges in modelling climate change impacts arise from limitations in our current knowledge of biodiversity. Community-level modelling can complement species-level approaches in overcoming these limitations and predicting climate change impacts on biodiversity as a whole. However, the community-level approaches applied to date have been largely correlative, ignoring the key processes that influence change in biodiversity over space and time. Here, we suggest that the development of new ‘semi-mechanistic’ community-level models would substantially increase our capacity to predict climate change impacts on biodiversity. Location Global. Methods Drawing on an expansive review of biodiversity modelling approaches and recent advances in semi-mechanistic modelling at the species level, we outline the main elements of a new semi-mechanistic community-level modelling approach. Results Our quantitative review revealed a sharp divide between mechanistic and non-mechanistic biodiversity modelling approaches, with very few semi-mechanistic models developed to date. Main conclusions We suggest that the conceptual framework presented here for combining mechanistic and non-mechanistic community-level approaches offers a promising means of incorporating key processes into predictions of climate change impacts on biodiversity whilst working within the limits of our current knowledge.

Summary 1. Planning for the conservation of river biodiversity must involve a wide range of management options and account for the complication that the effects of many actions are spatially removed from these actions. Reserve design algorithms widely used in conservation planning today are not well equipped to address such complexities. 2. We used process-based models to estimate the expected persistence of river biodiversity under alternative catchment-wide management scenarios and applied it in the Hunter Region (37 000 km²) in southeastern Australia. 3. The biological condition of 12 197 subcatchments was estimated using a multiple linear regression model that related assessments of the integrity of macroinvertebrate assemblages to human-induced disturbances at river sites. The best-fit model (R2 = 0.76) used measures of both local and catchment-wide disturbances as well as elevation and distance from source as predictor variables. Based on the outputs of this model, we estimated that substantial loss of river biodiversity had occurred in some parts of the coastal fringes and the lower parts of the larger river systems. The most affected river type was small, low-gradient streams. 4. The predicted biodiversity condition together with river types based on macroinvertebrate assemblages and abiotic attributes was used to estimate a biodiversity persistence index (BDI). 5. A priority value for each subcatchment was calculated for different actions by changing the disturbance values for that subcatchment and calculating the resulting marginal change in regional BDI. Maps were thereby created for three different types of priority: catchment protection priority, catchment restoration priority and river section conservation priority. 6. The subcatchments of high catchment protection priority for river biodiversity were mostly in the uplands and within protected areas. The river sections of high conservation priority included many coastal lowland rivers in and around protected areas as well as many upland headwater streams. Subcatchments of high priority for catchment restoration were mostly in coastal areas or lowland floodplains. 7. This approach may be particularly well suited to guide the integrated implementation of three place-based protection strategies proposed for freshwaters: focal areas, critical management zones and catchment management zones.

1. Classifications that group rivers and streams with similar ecological characteristics are used increasingly to underpin conservation and resource management planning. Uses include identifying systems that may respond similarly to human activities or management actions, setting guidelines and standards to manage human impacts, interpreting data from inventory (survey) and monitoring, and identifying priority sites for conservation management.2. Traditional approaches to river classification have been based mostly on delineating landscape units (ecoregions), often by grouping adjacent catchments having similar ecological character. However, use of this approach can be complicated by marked local heterogeneity of river systems. Instead, classifications may be more ecologically relevant if individual river or stream segments having similar environmental conditions are grouped together, independent of their geographic locations. The latter approach also allows the use of more rigorous classification procedures, including newly emerging techniques that optimise the ability of a classification to discriminate patterns in parallel sets of biological data.3. Here, we explore the use of one of these newer techniques, generalised dissimilarity modelling (GDM), an extension of generalised regression techniques, that defines an optimal set of transformations of candidate environmental predictors to maximise explanation of species turnover in site-based biological data.4. Using two biological data sets describing the distributions of freshwater fish and macroinvertebrates and a candidate set of functionally relevant environmental variables, we used GDM to identify the variables, weightings and transformations that best explain biological dissimilarities across sites. We then used these as input to a multivariate classification of 567000 river and stream segments throughout New Zealand. Weightings and transformations of these variables were also specified from the GDM analysis. The matrix of transformed environmental predictors was classified in a two-stage process, using non-hierarchical mediod clustering to define an initial set of 400 groups, with relationships between these groups then defined using hierarchical clustering.5. The resulting classification better discriminates sites with similar biological character than previous classifications, particularly at higher levels of classification detail. Key factors contributing to this success include the use of detailed, segment-specific environmental variables, coherently accounting for the longitudinal connectivity inherent in rivers including its implications for the construction of biologically relevant predictors, and the use of a modelling technique (GDM) designed to specifically analyse biological turnover and its relationships with environment. © 2010 Blackwell Publishing Ltd.

We compared seven scenarios for selecting incentive proposals, and evaluated their contribution to regional conservation outcomes. Scenarios ranged from strategic site assessment where proposals were ranked on scores from site-scale data and a priori regional value to systematic regional assessment with selection of proposals using optimization algorithms. The selection scenarios were simulated using proposals from a conservation investment program in South-eastern Australia. All selection scenarios provided a positive gain in biodiversity compared with selecting no proposals. Regional assessments provided ∼4% gain in the biodiversity index over site assessments. The performance of scenarios using optimization was similar to scenarios using rankings. A framework for integrating site and regional approaches for selecting proposals for conservation management is proposed, where proposals are assessed on their contribution to regional biodiversity outcomes rather than just on the comparative gain in biodiversity at the site, and regional conservation priorities are updated between rounds of incentive programs.

Question: Can species compositional dissimilarity analyses be used to assess and improve the representation of biodiversity patterns in a priori ecological classifications?

Aim To describe a general modelling framework for integrating multiple pattern- and process-related factors into biodiversity conservation assessments across whole landscapes. Location New South Wales (Australia), and world-wide. Methods The framework allows for a rich array of alternatives to the target-based model traditionally underpinning systematic conservation planning and consists of three broad modelling components. The first component models the future state (condition) of habitat across a landscape as a function of present state, current and projected pressures acting on this state, and any proposed, or implemented, management interventions. The second component uses this spatially explicit prediction of future habitat state to model the level of persistence expected for each of a set of surrogate biodiversity entities. The third component then integrates these individual expectations to estimate the overall level of persistence expected for biodiversity as a whole. Results Options are explored for tailoring implementation of the framework to suit planning processes varying markedly in purpose, and in availability of data, time, funding and expertise. The framework allows considerable flexibility in the nature of employed biodiversity surrogates (species-level, discrete or continuous community-level) and spatial data structures (polygonal planning units, or fine-scaled raster), the level of sophistication with which each of the three modelling components is implemented (from simple target-based assessment to complex process-based modelling approaches), and the forms of higher-level analysis supported (e.g. optimal plan development, priority mapping, interactive scenario evaluation). Main conclusions The described framework provides a logical, and highly flexible, foundation for integrating disparate pattern- and process-related factors into conservation assessments in dynamic, multiple-use landscapes.

We demonstrate a novel method for spatial conservation prioritization at a community-level that takes account of: (i) an environmentally-based classification of the landscape into community classes; (ii) similarities between community classes to allow for community complementarity-based selection; (iii) variation in species richness; (iv) variation in human impacts on ecological integrity; and (v) requirements for maintenance of upstream–downstream connectivity in riverine systems. While this technique has generic application, we demonstrate its application using a biologically-trained environmental classification of New Zealand’s river network. Our analysis produces a priority ranking of planning units (here 4th order catchments or sub-catchments) and performance estimates in terms of expected biodiversity returns given varying degrees of geographic protection. Accounting for community similarity ensures high protection for distinct habitat classes with low similarity to other classes; our results indicate a 28% loss in conservation efficiency of the highest-ranked 10% of the landscape if it is ignored. Accounting for human pressures and connectivity also had clear influences on spatial priority rankings, indicating the need to consider these factors in the conservation planning process.