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A global biodiversity observing system to unite monitoring and guide action
Abstract
The rate and extent of global biodiversity change is surpassing our ability to measure, monitor and forecast trends. We propose an interconnected worldwide system of observation networks — a global biodiversity observing system (GBiOS) — to coordinate monitoring worldwide and inform action to reach international biodiversity targets.
Supplementary resources (2)
Data
August 2023
Data
August 2023
... This process results in an active learning feedback loop (Fig. 1), in which hypotheses proposed by both humans and algorithms are validated and the results are used to train further algorithms with improved performance. Such active learning processes are predicted to be instrumental in the design of future global monitoring networks 38 . Contributions from AI might, therefore, not only increase the rate of species discovery and description but also the effectiveness of current and future generations of taxonomists. ...
... Extreme edge computational approaches increasingly move AI to the sensors themselves, in the form of smart camera traps and acoustic arrays that enable automated and adaptive data collection. Advanced techniques are also needed for spatial bias correction 38,88 , to improve models of undersampled species that are based on well sampled species 89,90 , and to model community assemblages and turnover 91 . The development of these methods requires cross-disciplinary collaborations of ecological statisticians and AI researchers 92 . ...
... We speculate that several potential avenues exist through which AI might begin to join human scientists as a collaborative research partner. For example, we see potential for the growth of AI-assisted methods that iteratively and adaptively optimize experimental sampling schemes, in concert with changing input from human researchers, which will feed into coordinated monitoring efforts such as those of Group on Earth Observations Biodiversity Observation Network (GEO BON) 38 . The use of AI in non-ecology fields, such as computer-aided drug discovery 185 and materials science 186 , has shown the potential for such models to propose candidate research directions for experimental follow-up. ...
... (g) Coverage of readily available new data Biodiversity science also trails climate science by decades in monitoring [34,68]. One result is that much of the effort needed to produce many biodiversity indicators-whether compiled or model-based-is spent finding and harmonizing relevant biodiversity observations that have already been made. ...
... Spatiotemporally resolved data are also lacking for many drivers of biodiversity change [50], undermining model-based indicators. Systematic monitoring of biodiversity and drivers, generating streams of harmonized essential variables in standardized formats, will transform the accuracy, precision and timeliness of compiled and model-based indicators alike [23,68,71], especially if some of the monitoring focuses on minimizing uncertainty [72]. Importantly, indicator estimates compiled from monitoring data can be used to validate model-based estimates (figure 1), highlighting model deficiencies. ...
... Importantly, indicator estimates compiled from monitoring data can be used to validate model-based estimates (figure 1), highlighting model deficiencies. In this context, the limited teleconnection of global biodiversity is beneficial rather than a handicap: monitoring in many different places will quickly compensate for the shortage of historical data, enabling more precise attribution of changes to drivers and strengthening models [68]. GEO BON's 'BON in a Box' initiative (https://boninabox.geobon.org/) ...
Georgina Mace proposed bending the curve of biodiversity loss as a fitting ambition for the Convention on Biological Diversity. The new Global Biodiversity Monitoring Framework (GBMF) may increase the chances of meeting the goals and targets in the Kunming–Montreal Global Biodiversity Framework (KMGBF), which requires bending the curve. To meet the outcome goals of KMGBF, the GBMF should support adaptive policy responses to the state of biodiversity, which in turn requires a ‘satnav’ for nature. The twin pillars of such a satnav are (i) models to predict expected future outcomes of today’s choices; and (ii) rapid feedback from monitoring to enable course corrections and model improvement. These same elements will also empower organizations to ensure that their actions are truly nature-positive, but they are not yet written into the GBMF. Without a satnav, society will effectively have to try to find its way to the outcome goals by looking in the rear-view mirror that the current headline indicators provide. Drawing contrasts and parallels with climate modelling, I discuss challenges for indicators, models, data and research culture that must be overcome if we are to bend the curve, and suggest ways forward.
This article is part of the discussion meeting issue ‘Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future’.
... Such information is essential to global assessments of biodiversity, such as those led by the IPBES (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services) and the IUCN (International Union for the Conservation of Nature), and also as input to inform decision-makers at various scales. Toward these ends, biodiversity observation networks (Gonzalez et al. 2023 ) obtain, funnel, and facilitate the near realtime analysis of oeld-derived data and remotely sensed observations to quantify the status of biodiversity and predict scenarios for its future. ...
... Our experiences derive from activities under the Group on Earth Observations Biodiversity Observation Network (GEO BON). GEO BON facilitates the development of national, regional, and thematic biodiversity observation networks (Gonzalez et al. 2023 ) and related software tools to connect end users and software tool developers worldwide. Importantly, GEO BON standardized a series of essential biodiversity variables to harmonize monitoring by leveraging remote sensing data sets (Pereira et al. 2013 ). ...
... In the present article, we leveraged two innovative tools and brought together a broad set of end users and developers to cocre-ate software that meets the needs for quantifying the essential biodiversity variable 8species distribution9. Critically, the Colombia Biodiversity Observation Network (Gonzalez et al. 2023 ) builds and maintains a vibrant community to develop biodiversity information systems at national and subnational scales. To promote biodiversity change indicator assessments, we partnered with the Colombia Biodiversity Observation Network to enhance the opensource, user-friendly, and modular SDM-building application Wallace EcoMod (Kass et al. 2018hereafter, Wallace). ...
Creating software tools that address the needs of a wide range of decision-makers requires the inclusion of differing perspectives throughout the development process. Software tools for biodiversity conservation often fall short in this regard, partly because broad decision-maker needs may exceed the toolkits of single research groups or even institutions. We show that participatory, collaborative codesign enhances the utility of software tools for better decision-making in biodiversity conservation planning, as demonstrated by our experiences developing a set of integrated tools in Colombia. Specifically, we undertook an interdisciplinary, multi-institutional collaboration of ecological modelers, software engineers, and a diverse profile of potential end users, including decision-makers, conservation practitioners, and biodiversity experts. We leveraged and modified common paradigms of software production, including codesign and agile development, to facilitate collaboration through all stages (including conceptualization, development, testing, and feedback) to ensure the accessibility and applicability of the new tools to inform decision-making for biodiversity conservation planning.
... Biodiversity loss has a significant impact on food, clean water, energy availability and other natural sources that contribute to the human wellbeing through the ecosystems (Pongsiri and Roman, 2007). Consequently, addressing biodiversity loss has become a global priority, as exemplified by Gonzalez et al. (2023) by proposing a global biodiversity observing system for improving the monitoring coordination worldwide. This system aims to prevent gaps and biases in biodiversity data and assess progress towards the Kunming-Montreal Global Biodiversity Framework (KM-GBF) goals, such as stopping human-induced species extinctions (Gonzalez et al., 2023). ...
... Consequently, addressing biodiversity loss has become a global priority, as exemplified by Gonzalez et al. (2023) by proposing a global biodiversity observing system for improving the monitoring coordination worldwide. This system aims to prevent gaps and biases in biodiversity data and assess progress towards the Kunming-Montreal Global Biodiversity Framework (KM-GBF) goals, such as stopping human-induced species extinctions (Gonzalez et al., 2023). A main drivers of biodiversity loss is related to the global food system such as land use, overfishing in oceans, unsustainable dietary models and increased food waste (Read et al., 2022). ...
... At the same time, knowledge can only grow and evolve through effective sharing and reuse. As scientific literature grows far faster than individual scientists can assimilate (Borycz and Carroll 2020) and researchers seek to provide timely, accurate, decision-relevant ES monitoring (Vaz et al., 2021;Balvanera et al.., 2022;Gonzalez et al., 2023a), solutions to effectively integrate and reuse knowledge are needed. ...
... ES assessments built upon FAIR data, models, and software are needed to support ES and ES-adjacent monitoring and accounting frameworks (section 4; Vaz et al., 2021;Balvanera et al.., 2022;Gonzalez et al., 2023a). In this future, as new input data are generated, ES models will be able to be re-run to provide continuously updated datasets and time series; as such data become increasingly timely, ES monitoring will move closer and closer to real-time. ...
Despite continued, rapid growth in the literature, the fragmentation of information is a major barrier to more timely and credible ecosystem services (ES) assessments. A major reason for this fragmentation is the currently limited state of interoperability of ES data, models, and software. The FAIR Principles, a recent reformulation of long-standing open science goals, highlight the importance of making scientific knowledge Findable, Accessible, Interoperable, and Reusable. Critically, FAIR aims to make science more transparent and transferable by both people and computers. However, it is easier to make data and models findable and accessible through data and code repositories than to achieve interoperability and reusability. Achieving interoperability will require more consistent adherence to current technical best practices and, more critically, to build consensus about and consistently use semantics that can represent ES-relevant phenomena. Building on recent examples from major international initiatives for ES (IPBES, SEEA, GEO BON), we illustrate strategies to address interoperability, discuss their importance, and describe potential gains for individual researchers and practitioners and the field of ES. Although interoperability comes with many challenges, including greater scientific coordination than today's status quo, it is technically achievable and offers potentially transformative advantages to ES assessments needed to mainstream their use by decision makers. Individuals and organizations active in ES research and practice can play critical roles in creating widespread interoperability and reusability of ES science. A representative community of practice targeting interoperability for ES would help advance these goals.
... Biodiversity loss poses a significant threat to ecosystems and human well-being, necessitating urgent global actions targeting effective conservation strategies and comprehensive biodiversity monitoring (Gonzalez et al. 2023a). The Kunming-Montréal Global Biodiversity Framework (GBF) of the United Nations Convention on Biological Diversity (CBD) defines the commitment by Parties to the CBD to protect and restore biodiversity, and maintain nature's contributions to people (Milner-Gulland et al. 2021, McGowan et al. 2024. ...
... data, country or region of interest, species, parameters of the model, etc.), press a button to run the code, and visualize and download the results. The BON in a Box platform is intended to transform GEO BON's current capacity to support parties in the implementation of the GBF's monitoring framework, including the establishment of a global biodiversity observing system (GBiOS, Gonzalez et al. (2023a)). ...
... Biodiversity loss poses a significant threat to ecosystems and human well-being, necessitating urgent global actions targeting effective conservation strategies and comprehensive biodiversity monitoring (Gonzalez et al. 2023a). The Kunming-Montréal Global Biodiversity Framework (GBF) of the United Nations Convention on Biological Diversity (CBD) defines the commitment by Parties to the CBD to protect and restore biodiversity, and maintain nature's contributions to people (Milner-Gulland et al. 2021, McGowan et al. 2024. ...
... data, country or region of interest, species, parameters of the model, etc.), press a button to run the code, and visualize and download the results. The BON in a Box platform is intended to transform GEO BON's current capacity to support parties in the implementation of the GBF's monitoring framework, including the establishment of a global biodiversity observing system (GBiOS, Gonzalez et al. (2023a)). ...
Biodiversity loss is a critical global challenge. The Kunming-Montréal Global Biodiversity Framework (GBF) sets ambitious goals to protect ecosystems, halt species loss, and enhance biodiversity. The GBF’s Monitoring Framework requires countries to track progress toward biodiversity targets using a standardized set of indicators that summarize complex trends in biodiversity. However, the calculation of these indicators can be challenging due to technical barriers, lack of available data and tools, and capacity bottlenecks, hindering countries’ ability to implement the monitoring framework. BON in a Box, developed by the Group on Earth Observations Biodiversity ObservationNetwork (GEO BON), is an open-source platform designed to address this challenge. It provides accessible tools for calculating Essential Biodiversity Variables (EBVs) and indicators — helping scientists, policymakers, and conservation practitioners prioritize monitoring areas, understand biodiversity trends, and track progress toward the targets of the GBF. BON in a Box automates the process of turning raw data into EBVs and indicators by connecting individual analysis steps into pipelines that can be run with minimal technical expertise. The pipelines are fully modular, customizable, open, and transparent, with options for users to use publicly available data or input their own proprietary data. Pipelines are all contributed by GEO BON collaborators, promoting knowledge sharing and scientific collaboration. BON in a Box is a collaborative platform for turning data into useful information to guide monitoring efforts, understand biodiversity change, make informed conservation decisions, and track progress toward meeting the targets of the GBF.
... an international network and data infrastructure that provides open access to data encompassing all forms of life on Earth 28 . The GBIF dataset stands as the world's largest biodiversity data network 45 and has been extensively used in biodiversity and phenology studies 46 , allowing us to conduct this study at a global scale. We obtained tree species occurrences following these steps: i) we removed the records from living specimens and conserved specimens; ii) queried all tracheophyte species and extracted georeferenced data records, excluding any coordinates with an abnormal latitude and longitude range; iii) standardized and harmonized scientific names of tracheophytes using the R package U.Taxonstand 47 ; iv) extracted all tree species occurrence records for the urban and rural areas of the selected cities. ...
Urban environments are typically warmer than surrounding rural areas, providing a unique setting for studying phenological responses to climate warming. Phenological differences between urban and rural trees are driven by local climate and species composition. Yet, the extent to which species composition influences phenological responses to urbanization remains poorly understood. To address this, we combine manipulative experiments, satellite-derived phenology data, and georeferenced tree occurrence records. Our findings show that, across Northern Hemisphere cities, differences in the temperature sensitivity of spring phenology between urban and rural areas are largely driven by urban-rural variation in species composition, surpassing the effects of preseason temperature. This pattern is particularly pronounced in Asian cities, where urban areas exhibit 0.74 ± 0.24 days/°C higher temperature sensitivity than rural areas. In-depth analyses using experiments and high-resolution satellite imagery from Beijing further demonstrate species-specific phenological responses to urbanization, with urban-dominant species exhibiting higher temperature sensitivity in urban environments compared to rural ones. These findings show that both interspecific variation in temperature sensitivity and species-specific responses to urbanization contribute to the pronounced impact of species composition on urban-rural phenological patterns. Our study underscores the importance of considering species composition when studying phenological responses to climate warming, especially in urban contexts.
... However, our understanding of how ecosystem services are changing remains limited (Vaz et al., 2021). Efforts are being made to develop systematic monitoring systems to track ecosystem service change (Tallis et al., 2012;Balvanera et al., 2022;Gonzalez et al., 2023a;Schwantes et al., 2024) but these face several challenges. One such challenge is the operationalisation of the concepts used in ecosystem service science to define and describe the variables to be monitored (de Groot et al., 2002;Bennett et al., 2015;Polasky et al., 2015;Bull et al., 2016). ...
Ecosystem services (ES) are an important part of global and national environmental policies. In this context, there is a call for the monitoring of ES to support their management. However, the proliferation of terms used within ES science is a barrier to standardised monitoring. Monitoring ES requires knowing exactly what variables to measure and how they relate to change in the states of ES. It further requires interoperability between methodologies used by information systems to operationalise data flows. Here, we aim to systematise the language used to define ES and the terminology used in their monitoring by developing an ontology for ES monitoring. Ontologies are tools that operationalise concepts and the relationships among terms used to define them. An ontology allows people and machines to use terms consistently. Building on advances in other disciplines , the ES monitoring ontology systematises the language of ES across major conceptual frameworks advancing conceptual clarity and operationalisation of ES. We test the ES monitoring ontology with data from three ES in British Columbia, Canada, to highlight how it can enable information sharing and monitoring. We show that the ontology can organise and retrieve information and data for ES monitoring in a systematic way. Our work contributes to advancing interoperability of ES, taking a step towards systematically understanding ES change with automated tools. We invite members of the ES community to join the effort of developing this ontology for ES so that can it contribute to the challenge of systematically monitoring change in social-ecological systems.
... Sustainable advancement of conservation genomics globally requires not only international collaborations between well-resourced and under-resourced countries but also local capacity building. This includes expanding training in molecular methods and bioinformatics, and redistribution of resources and financial support so that genomic studies can be led by Global South researchers (Asase et al. 2022;Barber et al. 2014;Gonzalez et al. 2023). Fortunately, successful models exist, including the ConGen workshop, which invites international participants and combines conceptual lectures with hands-on analysis practice led by conservation genomics experts (Stahlke et al. 2020), the Nigerian Bioinformatics and Genomics Network, which fosters training of Nigerian researchers and facilitates international collaborations (Fatumo et al. 2020), and the Amphibian Genomics Consortium, an international effort to increase access to genomics knowledge and research collaborations (Kosch et al. 2024). ...
Advances in genomic sequencing have magnified our understanding of ecological and evolutionary mechanisms relevant to biodiversity conservation. As a result, the field of conservation genomics has grown rapidly. Genomic data can be effective in guiding conservation decisions by revealing fine‐scale patterns of genetic diversity and adaptation. Adaptive potential, sometimes referred to as evolutionary potential, is particularly informative for conservation due to its inverse relationship with extinction risk. Yet, global coldspots in genomic resources impede progress toward conservation goals. We undertook a systematic literature review to characterise the global distribution of genomic resources for amphibians and reptiles relative to species richness, IUCN status, and predicted global change. We classify the scope of available genomic resources by their potential applicability to global change. Finally, we examine global patterns of collaborations in genomic studies. Our findings underscore current priorities for expanding genomic resources, especially those aimed at predicting adaptive potential to future environmental change. Our results also highlight the need for improved global collaborations in genomic research, resource sharing, and capacity building in the Global South.
... In this manuscript, we present SpeciesDistributionToolkit (abbreviated as SDT), a meta-package for the Julia programming language, offering an integrated environment for the retrieval, formatting, and interpretation of data relevant to the modeling of species distributions. SDT was in part designed to work within the BON-in-a-Box project (Gonzalez et al. 2023, Griffith et al. 2024), a GEO BON initiative to facilitate the calculation and reporting of biodiversity indicators supporting the Kunming-Montréal Global Biodiversity Framework. A leading design consideration for SDT was therefore to maximize interoperability between components and functionalities from the ground up. ...
(1) Species distribution modeling requires to handle varied types of data, and benefits from an integrated approach to programming. (2) We introduce SpeciesDistributionToolkit, a Julia package aiming to facilitate the production of species distribution models. It covers various steps of the data collection and analysis process, extending to the development of interfaces for integration of additional functionalities. (3) By relying on semantic versioning and strong design choices on modularity, we expect that this package will lead to improved reproducibility and long-term maintainability. (4) We illustrate the functionalities of the package through several case studies, accompanied by reproducible code.
... Could genomics and epigenetics offer deeper insights into genotypeto-phenotype relationships, improving our understanding of climate change adaptation and prioritizing populations for conservation at the range level? Furthermore, could technological innovations facilitate a 'macroscope' for biodiversity analysis and monitoring (Gonzalez et al. 2023), bridging the gap that often leaves the Global South underrepresented in our global datasets? These questions only scratch the surface of what could be achieved as we push the boundaries of predictive biogeography. ...
... Biodiversity also has a more general effect on ecosystem stability and resilience, serving as insurance for balancing out and recovering from unexpected events (Loreau et al. 2003, Folke et al. 2004 ). Furthermore, linking the observed changes in biodiversity and ecosystem services to drivers and pressures (causal attribution) requires the understanding of causal networks that relate change in variables to outcomes for people and nature (Gonzalez et al. 2023a, Mori et al. 2023. This is challenging because we often lack data linking drivers to biodiversity and ecosystem variables at the right scales, and both data and models come with considerable uncertainties (Gonzalez et al. 2023b ). ...
... The unfolding crisis of climate driven biodiversity loss has the potential to go unnoticed, especially in poorly monitored or understudied parts of the world, until it is too late to remediate impacts and protect species. The development of a global biodiversity monitoring network has been proposed multiple times [16][17][18][19][20][21][22] but operationalizing these ambitions and prioritizing associated efforts remains to be developed. A handful of specialized monitoring systems have been designed for certain ecosystems such as forests (www.global ...
2023 was the hottest year in recorded history at the time of its recording ¹ and warmer than any in the past 125,000 years ² . Although the effects of this unprecedented year on human health, agriculture, and economies have been documented ³ , we know much less about its effects on global biodiversity, especially in poorly monitored regions. Here, we demonstrate a rapid climate bioassessment pipeline to pinpoint when and where species have recently been exposed to extreme weather. Applying this approach to > 33,000 terrestrial vertebrate species, we demonstrate that 2023 posed unprecedented levels of risk to biodiversity, with half of all species exposed to extreme temperatures somewhere in their geographic range and 1 in 10 exposed across > 25% of their range. We show that exposure to extreme weather has increased rapidly over the last decade and that many species now exist dangerously close to their historical niche limits. Consequently, although the global mean annual temperature in 2023 was only 0.2 o C warmer than the previous warmest year on record in 2016, species exposure doubled. Our 2023 vertebrate assessment provides a prototype for a highly flexible pipeline that can be extended to accommodate any pertinent weather data collected in real-time and can be customized for regional, taxonomic, or conservation-specific needs. Our pipeline can be used to direct management resources to those ecosystems and species, particularly in poorly monitored regions, that are at risk of unnoticed collapse, decline, or extinction following exposure to unprecedented conditions.
... The BON networks operate within a set of essential biodiversity variables (EBVs) that are used to identify indicators for biodiversity (Zilioli et al., 2021;GEOBON, 2023). Additionally, national BONs monitor biodiversity and plan for a global biodiversity observing system (GBiOS) that could interlink existing capacities and organizations in the national governments (Gonzalez et al., 2023). In general, thematic ocean observation networks serve to communicate a consensus about ocean health from the international scientific community within a specific field to inform policy and accelerate actions. ...
Increasing global temperatures, biodiversity loss, and pollution are among the major transformative changes affecting terrestrial and marine ecosystems. The marine biome can be observed and monitored using ocean observations with satellite-based platforms, tagging of animals, autonomous platforms and vehicles, and ship-based measurements. Sustaining ocean observations at a given location over time are known as ocean time series. For example, stationary platforms like moorings record data autonomously at one location over time, while ship-based time series stations are sampled using discrete measurements at varying sampling frequencies throughout a given year. Ocean observations feed into networks that either focus on obtaining data, e.g., similar instruments, or a social focus that aims to connect participants, e.g., early career or science-policy interface. These platforms and associated networks feed into the Framework for Ocean Observing (FOO) with a set of Essential Ocean Variables (EOVs) and the Ocean Best Practices System (OBPS) developed by the global ocean observing community. The range of ocean observing activities around the world creates a complex landscape, which can be particularly difficult to navigate for early career ocean professionals (ECOPs). ECOPs face higher barriers to entering the United Nations Decade of Ocean Science for Sustainable Development (“UN Ocean Decade”) than others despite actively contributing to ocean observations and playing the most significant role in the success of sustainability transformations. The review aims to provide an overview of the organizations and networks associated with ocean observations, motivate ECOPs to actively get involved in the ongoing UN Ocean Decade (2021-2030), and join or create new initiatives within the existing landscape. Ocean observations harbor an extraordinary benefit for a large diversity of end-users far beyond the scientific community, and there is a need to engage the next generation of ocean leaders as we transition to live and manage this blue planet sustainably.
... Consequently, monitoring species range shifts using discontinuous transects in mountain ecosystems may introduce biases of unknown magnitude. Some studies even suggested that the rapid and extensive nature of species range shifts may outpace our current monitoring ability [20], hindering our understanding of how species respond to climate change and the underlying mechanisms. ...
Climate change is compelling species to seek refuge at higher elevations and latitudes. While researchers commonly study these migrations using discontinuous elevational transects, this methodology may introduce significant biases into our understanding of species movement. These potential biases could lead to flawed biodiversity conservation policies if left unexamined. To address this concern, we utilized species distribution data from a novel continuous elevational transect to evaluate the accuracy of discontinuous transect methods. Our analysis focused on how quadrat spacing and survey time intervals affect bias in estimating species range shifts. The results were striking: the widely used settings for discontinuous transects failed to detect 7.2% of species, inaccurately estimated shift distances for 78% of species, and produced an overall error rate of 86%. Wider quadrat spacing increased these error rates, while longer survey intervals generally reduced them. Moreover, discontinuous transects consistently underestimated species shift distances, with this underestimation becoming more pronounced over longer survey periods. Our pioneering assessment of bias in discontinuous elevational transects demonstrates that a 50 m quadrat spacing combined with a 60-year survey interval optimizes monitoring species range shifts for conservation planning. This baseline protocol could be further strengthened through supplementary, frequent surveys targeting high-elevation species—a strategic approach that maximizes accuracy while maintaining cost-effectiveness.
... In December 2022, countries agreed to the Kunming-Montreal Global Biodiversity Framework's ambitious targets to halt and reverse biodiversity loss by 2030 (Maney et al., 2024). To achieve this goal, it is crucial we develop effective monitoring tools that can be used to survey biodiversity patterns over large areas and long periods of time (Gonzalez et al., 2023). A promising approach for surveying vocalizing animals, such as birds, is the use of passive acoustic monitoring (PAM) methods (Stephenson, 2020;Stowell and Sueur, 2020). ...
Human activities are accelerating biodiversity loss, necessitating tools capable of monitoring biodiversity patterns over large spatial and temporal scales. Passive acoustic monitoring methods, including acoustic indices, are emerging as a promising approach for surveying vocalizing animals. Numerous studies have assessed the effectiveness of acoustic indices in surveying animal communities, focusing mostly on birds and seven commonly used indices, yielding mixed results. Combining the indices has been proposed as a solution to produce more accurate predictions. In this study, we use data from 114 biodiverse sites in three countries, Cyprus, China, and Australia, to evaluate the combined effectiveness of sixty different acoustic indices in measuring bird species richness. Using the Boruta feature selection algorithm and random forest regressors, we find that the effectiveness of the indices varies considerably across study areas, and it is generally lower than what would be required to monitor bird species richness accurately (R2Cyprus = 0.06, R2China = 0.31, R2Australia = 0.52). Moreover, the most useful set of indices varied for each area; none of the sixty indices were useful in all three areas, and only three indices were useful in more than one area. Our findings, along with those of other recent studies, suggest that acoustic indices may not currently be an effective method for accurately monitoring bird species richness despite their utility in other applications, such as surveying broader biodiversity patterns. Moreover, we recommend that whenever researchers investigate the efficacy of acoustic indices, they consider all available indices to identify the most useful in their study region.
... Yu et al., 2021). Successful examples are in urban heat island mitigation (Mendez-Astudillo et al., 2021), biodiversity monitoring (Gonzalez et al., 2023), coastal erosion management (Athanasiou, 2022;Verhagen & Pilarzcyk, 1990), drought prediction (Abraham et al., 2017B. Su et al., 2017), and wastewater treatment (Fenner & Stuetz, 1999), which emphasize the importance of advanced technologies and continuous monitoring for effective environmental management (Reljic et al., 2023). ...
Environmental monitoring and management are critical for sustainable development and the preservation of natural resources. Recent advancements in artificial intelligence (AI) integrated with geospatial technologies promise to revolutionize these fields. Delving into potential breakthroughs, AI offers precise, real-time monitoring of environmental parameters through machine learning (ML) algorithms, remote sensing data, and geographic information systems (GIS). Enhanced data analysis techniques facilitate the early detection of environmental anomalies, predictive modeling of ecological trends, and efficient resource management. Successful implementations of AI in tracking climate change impacts, managing natural disasters, and monitoring biodiversity are presented through various case studies. Challenges such as data privacy, algorithm transparency, and the need for interdisciplinary collaboration are also addressed. Future research directions explore AI’s potential to foster more resilient and adaptive environmental management practices. Synthesizing AI and geospatial technology underscore a transformative approach to safeguarding our environment.
... For example, only one global scenario modelling effort has been integrated into IPBES so far and it was not a major focus of the assessment 32 . That said, the Group on Earth Observation's Biodiversity Observation Network (GEO BON) has proposed a global biodiversity observing system (GBiOS), which includes the goal of increasing the capacity to forecast biodiversity change and the loss of ecological and evolutionary resilience 33 . GEO BON has also launched a new working group (EcoCode) with the aims of synthesizing biodiversity modelling tools, developing shared platforms and creating a biodiversity model intercomparison platform. ...
... With the advent of big data, collaborative networks are becoming increasingly capable of addressing questions that depend on large volumes of data (Gorelick et al 2017). For example, biodiversity assessments through the Global Biodiversity Information Facility (GBIF) and similar collaborative networks could be considered to characterize regional processes, or to integrate and enrich local data from permanent plots (Cuesta, Carilla, et al 2023;Gonzalez et al 2023). The lack of high-quality local data, in addition to limited data access, which is essential to calibrating large-scale datasets, usually constitutes a limiting condition. ...
... The GBF emphasizes the need for improved data collection, monitoring, and information sharing at the national level to support decision-making. The framework can guide further development and harmonization of national data collection and reporting efforts using a standard set of essential biodiversity variables that directly informs indicators for monitoring change in biodiversity and ecosystem function (Perino et al. 2022, Gonzalez et al. 2023. The approach of the Kunming-Montreal GBF can therefore initiate further stimulation and development of NBDIs. ...
Today, at the international level, powerful data portals are available to biodiversity researchers and policymakers, offering increasingly robust computing and network capacities and capable data services for internationally agreed-on standards. These accelerate individual and complex workflows to map data-driven research processes or even to make them possible for the first time. At the national level, however, and alongside these international developments, national infrastructures are needed to take on tasks that cannot be easily funded or addressed internationally. To avoid gaps, as well as redundancies in the research landscape, national tasks and responsibilities must be clearly defined to align efforts with core priorities. In the present article, we outline 10 essential functions of national biodiversity data infrastructures. They serve as key providers, facilitators, mediators, and platforms for effective biodiversity data management, integration, and analysis that require national efforts to foster biodiversity science, policy, and practice.
... These experiments also provide a platform for integrating remote sensing tools with measured biological processes on the ground and for advancing capabilities to monitor forest growth, diversity, and ecosystem function (Cavender-Bares et al., 2020Williams et al., 2021). Such tools are urgently called for to advance the global monitoring of biodiversity and ecosystem function (Gonzalez et al., 2023). ...
We introduce a new “ecosystem‐scale” experiment at the Cedar Creek Ecosystem Science Reserve in central Minnesota, USA to test long‐term ecosystem consequences of tree diversity and composition. The experiment—the largest of its kind in North America—was designed to provide guidance on forest restoration efforts that will advance carbon sequestration goals and contribute to biodiversity conservation and sustainability.
The new Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity and functional diversity planted in 100 m² and 400 m² plots at 1 m spacing, appropriate for testing long‐term ecosystem consequences. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of 12 species was planted in 16 m² plots at 0.5 m spacing. Both are adjacent to the BioDIV prairie‐grassland diversity experiment, enabling comparative investigations of diversity and ecosystem function relationships between experimental grasslands and forests at different planting densities and plot sizes.
Within the first 6 years, mortality in 400 m² monoculture plots was higher than in 100 m² plots. The highest mortality occurred in Tilia americana and Acer negundo monocultures, but mortality for both species decreased with increasing plot diversity. These results demonstrate the importance of forest diversity in reducing mortality in some species and point to potential mechanisms, including light and drought stress, that cause tree mortality in vulnerable monocultures. The experiment highlights challenges to maintaining monoculture and low‐diversity treatments in tree mixture experiments of large extent.
FAB2 provides a long‐term platform to test the mechanisms and processes that contribute to forest stability and ecosystem productivity in changing environments. Its ecosystem‐scale design, and accompanying R package, are designed to discern species and lineage effects and multiple dimensions of diversity to inform restoration of ecosystem functions and services from forests. It also provides a platform for improving remote sensing approaches, including Uncrewed Aerial Vehicles (UAVs) equipped with LiDAR, multispectral and hyperspectral sensors, to complement ground‐based monitoring. We aim for the experiment to contribute to international efforts to monitor and manage forests in the face of global change.
... In response to this crisis, numerous efforts have been initiated worldwide to halt and reverse the decline in biodiversity, and there is a need for standardized, robust biodiversity monitoring programs. Such programs are essential for accurately assessing the status and trends of biodiversity, for evaluating the effectiveness of conservation management, and to inform policy decisions (Yoccoz et al. 2001;Gonzalez et al. 2023b). To be effective, optimizing monitoring efforts and their implementation across diverse ecosystems and representatively covering organism groups remains crucial. ...
Implementing and optimizing biodiversity monitoring is crucial given the current, worldwide biodiversity decline. Compared to other ecosystems, monitoring of biodiversity is lagging behind in groundwater ecosystems, both because of sparse taxonomic knowledge and methodological constraints. We here assessed temporal variation in the occurrence and abundance of macroinvertebrates collected systematically from shallow groundwater aquifers of Switzerland to establish general principles on seasonality and repeatability of assessment outcomes. We found no seasonal abundance pattern for obligate groundwater amphipods and isopods, indicating temporal consistency. In contrast, other macroinvertebrates (predominantly stygophiles and stygoxenes) showed pronounced seasonality in their detection rate. However, we found variability in detection rates across groundwater amphipod species and especially across sampling sites. For groundwater communities, characterized by narrowly-distributed and rare species, our results highlight the need for tailored and extensive sampling strategies. When setting up monitoring programs on groundwater fauna, detection probability, temporal autocorrelation, and standardization of sampling effort should be carefully considered. Applying novel, systematic approaches, can offer promising methodologies for understanding and conserving groundwater ecosystems.
... These classifications are used in nature conservation and restoration, biomonitoring and landscape management. They allow us to extrapolate reference conditions to sites for which we lack data on pristine conditions (Moog et al., 2004;Reynoldson et al., 1997), to approximate species ranges (Pinkert et al., 2023), to enable the proper design of large-scale probabilistic surveys (Hawkins & Yuan, 2016) and to derive policyrelevant information (Gonzalez et al., 2023). However, a classification is only appropriate for these uses if variation in assemblage composition is less pronounced within than between the ecosystem types it delineates. ...
For nature conservation and planning, terrestrial ecosystems are commonly classified based on their plant communities. Although soils are fundamental to ecosystem functioning, ecosystem classifications based on soil organisms are rare, and it is poorly understood whether their assemblage compositions follow existing classification schemes. We examined whether commonly used ecosystem types capture variation in earthworm (Lumbricidae) assemblages—a crucial biotic component of soil ecosystems. To this end, we created four ecosystem classifications by combining large‐scale climatic classifications (Biogeographic Regions [BGR] and Holdridge Life Zones [HLZ]) with small‐scale land cover classifications (CORINE Land Cover [CLC] and European Nature Information System [EUNIS]). European earthworm assemblage data from the sWORM and Edaphobase databases were analysed for variation in composition within and among ecosystem types, using Permutational Analysis of Variance and Analysis of Similarities. Additionally, we used Typical Species Analysis to establish typical earthworm assemblages (TAs) for each ecosystem type. Ecosystem classifications using the BGR explained more variance than HLZ, but HLZ showed a higher separation of assemblages between ecosystem types. The differentiation between Atlantic and Continental climates in the BGR could explain the superiority over the HLZ, which had only one category for the cool temperate zone of our study region. The typical assemblages contained on average six species, with some habitat generalists present in most. This study shows that combinations of ecosystem properties from different spatial scales can be used to distinguish between earthworm assemblages at the European level. However, earthworm assemblages across Europe were highly similar due to low species richness and the dominance of a few widespread species. This limits the possibility of applying TAs on large spatial scales, for example, for environmental monitoring. We suggest that future studies should explore the use of more species‐rich groups of soil organisms to characterize ecosystem types.
... A GBiOS would support the implementation national biodiversity monitoring networks 34,37,73,74 while assembling an international system similar to that used to monitor the oceans (i.e. the Global Ocean Observing System) or trends in climate and weather (WMO Integrated Global Observing System) that benefit all countries 75 . Following this model, GBiOS would therefore exist as a federated international network of existing and new national monitoring systems 63, 74 . Over time, GBiOS would allow multilevel assessments of trends in different facets of biodiversity (genetic, species and ecosystem) that could contribute to calculating indicators and the use of robust models needed to assess policy scenarios. ...
The Kunming-Montreal Global Biodiversity Framework (GBF) is the most ambitious agreement on biodiversity conservation and sustainable use to date. It calls for a whole-of government and whole-of-society approach to halt and reverse biodiversity loss worldwide. The Monitoring Framework of the GBF lays out how Parties to the Convention on Biological Diversity (CBD) are expected to report their progress. A CBD expert group provided guidance on its implementation, including a gap analysis to identify the strengths and limitations of the indicators in the Monitoring Framework. We present the results of the gap analysis, highlight where more work is needed and provide recommendations on implementing and improving monitoring to allow effective and comprehensive tracking of the GBF’s ambition. We find that with the headline and binary indicators, which Parties are required to use, the Monitoring Framework fully covers 19% of the GBF’s ambition and partially covers an additional 40%. Including disaggregations of the headline indicators improves coverage to 22% fully and an additional 41% partially. Adding optional (component and complementary) indicators brings full coverage to 29% with an additional 47% partial coverage. No indicators are available for 12% of the GBF. In practice, the coverage of the Monitoring Framework will depend on which indicators (headline and binary as well as component and complementary) and disaggregations are used by countries. Disaggregations are particularly relevant to monitor the cross-cutting considerations defined under section C. Substantial investment is required to collect the necessary data to compute indicators, infer change, and effectively monitor progress. We highlight important next steps to progressively improve the efficacy of the Monitoring Framework.
... This includes serving key international, regional, and national policy processes supporting decisions for national governments and policy makers but also connecting to other global initiatives such as the Global Biodiversity Observing System (GBiOS). GBiOS is a proposal from the Group on Earth Observations Biodiversity Observation Network (GEO BON) and its partners, that would combine technology, data, and knowledge from around the world to foster collaboration and data sharing among countries [33]. ...
Protected and conserved areas are a key area-based strategy to address the biodiversity and climate change crises. Indicators are fundamental to understanding performance over time. The Digital Observatory for Protected Areas (DOPA) was born in 2013 as a set of open-access web services and applications to be used to assess, monitor, and report on protected and conserved areas. For over a decade, it has delivered over 50 indicators to support policy processes, national and regional governments, and practitioners. DOPA has also developed a versatile and efficient back-end approach that is widely applicable in other area-based conservation contexts. Here, we describe the methods and workflows behind DOPA's back end and provide examples of policy relevant questions it can answer. We discuss the key advantages and limitations of this systematic and replicable approach and explore the use of this back-end architecture to inform progress in area-based conservation targets for the following decades. This approach, embedded in multiple services provided by the Knowledge Centre for Biodiversity of the European Commission (KCBD), can also support the implementation and monitoring of area-based targets of the Kunming Montreal Global Biodiversity Framework at international, regional, and national levels.
... Whether human societies can mitigate and cope with this biodiversity crisis is uncertain, but actions taken to tackle the problem should be based on sound scientific knowledge of the biosphere state and functioning (O'Connor et al., 2022). Thus, reliable spatial and temporal information on biodiversity response to human threats and conservation action is of paramount importance to guide decision making through this crisis (Pereira and Cooper, 2006;Xu et al., 2021;Lehmann et al. 2022;Gonzalez et al., 2023). For example, the decision over whether biodiversity is more effectively protected by promoting sustainable practices across entire landscapes versus dividing the landscapes between intense production and preservations areas requires an understanding of the trends of biodiversity at scales ranging from individual properties to regional protected areas (Sidemo-Holm et al., 2021). ...
... This includes serving key international, regional, and national policy processes supporting decisions for national governments and policy makers but also connecting to other global initiatives such as the Global Biodiversity Observing System (GBiOS). GBiOS, is a proposal from the Group on Earth Observations Biodiversity Observation Network (GEO BON), and its partners, that would combine technology, data, and knowledge from around the world to foster collaboration and data sharing among countries (Gonzalez et al., 2023). ...
Protected and conserved areas are a key area-based strategy to address the biodiversity and climate change crises. Fundamental to understand their performance over time are indicators. The Digital Observatory for Protected Areas (DOPA) was born in 2013 as a set of open access web services and applications to be used to assess, monitor, and report on protected and conserved areas. For over a decade it has delivered over 50 indicators to support policy processes, national and regional governments, and practitioners. DOPA has also developed a versatile and efficient back-end approach that is widely applicable in other area-based conservation contexts. Here, we describe the methods and workflows behind DOPA´s back end and provide examples of policy relevant questions it can answer. We discuss key advantages and limitations of this systematic and replicable approach and explore the use of this back-end architecture to inform progress in area-based conservation targets for the following decades. This approach, embedded in multiple services provided by the Knowledge Centre for Biodiversity of the European Commission (KCBD), can also support the implementation and monitoring of area-based targets of the Kunming Montreal Global Biodiversity Framework at international, regional, and national levels.
... The Kunming-Montreal Global Biodiversity Framework adopted in December 2022 highlights the need for developing a global biodiversity observing system (Gonzalez et al., 2023). This requires effective and cost-efficient monitoring techniques and a dramatic increase in the spatial, temporal, and taxonomic extent of biodiversity monitoring (Besson et al., 2022;Kissling et al., 2018;. ...
Development of a cost-efficient automated wildlife camera network in the Amsterdam Dunes , a European Natura 2000 site. This approach provides opportunities for studying the distribution, habitat use, activity, phenology, population structure and community composition of wildlife species and allows comparison of traditional with the novel monitoring approach.
... In December 2022, countries worldwide committed to the ambitious targets of the Kunming-Montreal Global Biodiversity Framework (GBF), aiming at halting and reversing biodiversity loss by 2030 2 . A crucial component of the framework is the proper monitoring of progress, including the monitoring of biodiversity patterns 3 . To achieve this, it is essential to develop effective monitoring technologies capable of assessing biodiversity across large spatial and temporal scales 3 . ...
There is growing interest in using novel technologies for large-scale biodiversity monitoring. Passive acoustic monitoring (PAM) represents a promising approach for surveying vocalizing animals. However, further development of PAM methods is needed to improve their accuracy. The availability of extensive ecoacoustic datasets from biodiverse areas can facilitate this development. In this study, we present a large ecoacoustic dataset (1.58 TB) collected at sixty-one study sites on the island of Cyprus between March and May 2023. The dataset comprises > 310,000 audio files, representing over 5,200 hours of recordings. It can be used for a range of applications, such as developing and refining species identification algorithms, acoustic indices, and protocols for processing acoustic data to exclude non-focal sounds, e.g., those produced by human activities. It can also be used to explore fundamental ecological questions. To facilitate its use, the complete dataset has been made available on the Hugging Face repository and the ARBIMON platform, operated by Rainforest Connection™, which offers a range of free tools for ecoacoustic analyses.
Agricultural expansion and intensification has caused habitat loss, contributing to the current biodiversity crisis. Reliable, efficient and consistent information at the farm-scale is critical to understand the magnitude of recent changes in biodiversity and to inform future management actions aimed at reversing historical declines. We apply a habitat-based biodiversity assessment approach to examine the potential for grazing farms across Australia to improve outcomes for biodiversity by revegetating 10 % of the farm area. Fourteen case-study farms distributed across Australia with diverse attributes were assessed, including an analysis of likely benefits for biodiversity 30 years after commencing a hypothetical revegetation scenario, within the context of estimates of recent historical changes. From 2004 to 2020, the three biodiversity indicators considered decreased for the majority of farms. The scenario for revegetating 10 % of the farm area was estimated to substantially increase the biodiversity indicators, with half of the farms estimated to achieve recovery for all 3 indicators to greater than 2004 levels by 2050. Smaller farms with lower average ecosystem condition in 2020 were estimated to achieve the greatest gains in biodiversity from the revegetation scenario, relative to their indicator values in 2020. Farm revegetation actions have substantial potential to improve outcomes for biodiversity, though such gains may be difficult and time consuming to achieve, emphasising the importance of avoiding further habitat loss through removal or degradation of native vegetation.
There is growing interest in using novel technologies for large-scale biodiversity monitoring. Passive acoustic monitoring (PAM) represents a promising approach for surveying vocalizing animals. However, further development of PAM methods is needed to improve their accuracy. The availability of extensive ecoacoustic datasets from biodiverse areas can facilitate this development. In this study, we present a large ecoacoustic dataset (1.58 TB) collected at sixty-one study sites on the island of Cyprus between March and May 2023. The dataset comprises >313,000 audio files, representing over 5,200 hours of recordings. It can be used for a range of applications, such as developing and refining species identification algorithms, acoustic indices, and protocols for processing acoustic data to exclude non-focal sounds, e.g., those produced by human activities. It can also be used to explore fundamental ecological questions. To facilitate its use, the complete dataset has been made available on the Hugging Face repository and the ARBIMON platform, operated by Rainforest ConnectionTM, which offers a range of free tools for ecoacoustic analyses.
Accurately identifying priority areas for the protection and restoration of water ecosystems is essential for refined management of national land space. The water ecological security pattern (WESP) integrates multiple ecological elements and serves as an effective approach for prioritizing conservation and restoration efforts. Taking Guangxi Hechi, a typical karst region in China, as the study area, we quantitatively evaluated different types of water ecological sources based on a “function–structure–resilience” framework. Then, we used circuit theory and surface runoff model to extract flood corridors and life corridors, further identifying priority areas for protection and restoration. The results showed that the WESP included 17 ecological sources (7,344.59 km²) in the form of strips and patches, as well as 24 flood corridors (769.30 km²) and 3 life corridors (5,138.02 km²). Priority protection areas included habitat important areas in the sources (4,952.96 km²) and 47 pinch points (393.51 km²) in the corridors. Priority restoration areas included ecologically fragile areas in the sources (1,913.19 km²) and barriers in the corridors (219.03 km²). Furthermore, the most urgent areas for water ecosystem management were overlapping patches of priority protection and restoration areas in both sources (478.44 km²) and corridors (45.33 km²). This study proposes a comprehensive framework for constructing a WESP and identifying priority areas within water ecosystems, offering an effective solution for watershed ecological protection and restoration in ecologically fragile areas worldwide.
The year 2030 is rapidly approaching. Building, monitoring, and reporting indicators to evaluate the 2030 targets in the Kunming-Montreal Global Biodiversity Framework (GBF) is a major challenge that requires, at minimum, nations to assess their progress at least once within the next five years. To effectively monitor this progress, we need indicators that capture fast-paced, on-the-ground biodiversity change at the scale of conservation action, alongside slower, more diffuse biodiversity trends at national scales. We argue that the focus on monitoring global and national biodiversity changes has left a gap in our ability to capture fine-scale changes and therefore our progress towards the GBF's Goal A targets. To fill this blank space, we recommend integrating locally sourced data into biodiversity indicators, testing indicator performance at relevant spatiotemporal scales, monitoring locally and strategically to detect changes across scales, and developing indicators of fine-scale biodiversity changes.
As global and regional vegetation diversity loss threatens essential ecosystem services under climate change, monitoring biodiversity dynamics and its role in ecosystem services is crucial in predicting future states and providing insights into climate adaptation and mitigation. In this context, remote sensing (RS) offers a unique opportunity to assess long-term and large-scale biodiversity dynamics. However, the development of this capability suffers from the lack of consistent, global, and spatially matched ground diversity measurements that enable testing and validating generalizable methodologies. The Biodiversity Observing System Simulation Experiment (BOSSE) aims to alleviate the lack of this information by means of simulation. BOSSE simulates synthetic landscapes featuring communities of various vegetation species whose traits´s seasonality and ecosystem functions (e.g., biospheric fluxes) respond to meteorology and environmental factors. Simultaneously, BOSSE can generate various types of remote sensing imagery linked to the traits and functions via radiative transfer theory. Specifically, it simulates hyperspectral reflectance factors (R), which can be convolved to the bands of specific RS missions, sun-induced chlorophyll fluorescence (SIF), and land surface temperature (LST). The resolution of the RS imagery can be degraded to test the robustness of different approaches to information loss and the capability of new methodologies to overcome this limitation. Therefore, BOSSE enables users to evaluate the capability of different methods to estimate plant functional diversity (PFD) from RS and link it to ecosystem functions. We expect BOSSE to support the benchmarking and improvement of old and novel methods dedicated to estimating plant diversity and exploring the biodiversity-ecosystem function (BEF) relationships, facilitating advances in this growing area of research and supporting the analysis and interpretation of real-world measurements. We also expect BOSSE to be extended and include new features that provide more realistic simulations that help answer more complex questions related to climate change and global warming.
Tracking biodiversity across biomes over space and time has emerged as an imperative in unified global efforts to manage our living planet for a sustainable future for humanity. We harness the National Ecological Observatory Network to develop routines using airborne spectroscopic imagery to predict multiple dimensions of plant biodiversity at continental scale across biomes in the US. Our findings show strong and positive associations between diversity metrics based on spectral species and ground-based plant species richness and other dimensions of plant diversity, whereas metrics based on distance matrices did not. We found that spectral diversity consistently predicts analogous metrics of plant taxonomic, functional, and phylogenetic dimensions of biodiversity across biomes. The approach demonstrates promise for monitoring dimensions of biodiversity globally by integrating ground-based measures of biodiversity with imaging spectroscopy and advances capacity toward a Global Biodiversity Observing System.
Biodiversity offsetting and ecosystem accounting are two rapidly developing fields that share a common goal: quantifying changes in ecosystems. Nevertheless, the intersection of these fields is often overlooked, despite the significant synergies they offer. This perspective paper explores this intersection from both sides, highlighting the benefits of ecosystem accounting for offsetting practice and the steps needed to make ecosystem accounts offsetting‐ready.
The System of Environmental‐Economic Accounting—Ecosystem Accounting (SEEA EA) is the most widespread and sophisticated framework for ecosystem accounting. This framework was designed to consistently quantify biodiversity changes at several spatial scales, including fine scales typically relevant for biodiversity offsetting. Furthermore, the components of this ecosystem accounting framework are also tightly related to the key concepts of biodiversity offsetting. To illustrate this, we provide a dictionary cross‐linking the terminologies of the two fields.
Despite the fundamental similarities, most ecosystem accounts developed today are not (directly) suitable for fine‐scale use. We discuss the reasons for this, and the practical challenges of improving the suitability of ecosystem accounts for offsetting use.
Solution: Aligning ecosystem accounts and biodiversity offsetting offers a huge opportunity for both fields, enhancing the standardisation of offsetting practices, and making them extensible to high level no net loss biodiversity targets. This can be achieved by using offsetting‐relevant scalable biodiversity metrics as condition variables, and by implementing condition indices that yield meaningful offsetting currencies. We argue that future ecosystem accounting case studies should recognise offsetting and the quantification of net loss/gain as relevant use cases. And we also call for dedicated offsetting pilots that apply ecosystem accounts in concrete no net loss contexts, highlighting the transformative potential of harnessing these synergies for biodiversity policy.
Land-use change drives biodiversity loss, but some species suffer more than others. Indicators of global biodiversity change must attempt to summarise these impacts representatively and meaningfully to guide biodiversity recovery. Yet species that are hard to detect, and thus feature less in relevant databases, might possess traits that make them particularly sensitive to anthropogenic impacts. Using global data for plant, bird, and spider species, we developed a statistical approach to analyse and correct for the impact of excluding hard-to-sample species from global biodiversity indicators. Based on over 4000 species with abundance comparisons available, we found that species with fewer global occurrence records consistently decline more as land-use intensity increases, suggesting that hard-to-sample species are particularly sensitive to land-use differences. When we extrapolate this relationship to all plant, bird and spider species with valid occurrence records (0.27 M species), we obtain a more representative global indicator of overall land-use impacts for these entire taxonomic groups. Our estimates indicate a lower average abundance in anthropogenic land uses compared to results obtained when hard-to-sample species are excluded. For example, intensive agriculture only has 18% of the biodiversity level of primary vegetation, rather than the 47% estimated without extrapolation. We recommend that other existing indicators include an extrapolation solution based on ours, to incorporate the available data as effectively as possible. Using occurrence data to predict species' sensitivity unlocks many possibilities to improve global biodiversity indicators, without demanding additional data on poorly known species.
Rapid growth in bio-logging-the use of animal-borne electronic tags to document the movements, behaviour, physiology and environments of wildlife-offers opportunities to mitigate biodiversity threats and expand digital natural history archives. Here we present a vision to achieve such benefits by accounting for the heterogeneity inherent to bio-logging data and the concerns of those who collect and use them. First, we can enable data integration through standard vocabularies, transfer protocols and aggregation protocols, and drive their wide adoption. Second, we need to develop integrated data collections on standardized data platforms that support data preservation through public archiving and strategies that ensure long-term access. We outline pathways to reach these goals, highlighting the need for resources to govern community data standards and guide data mobilization efforts. We propose the launch of a community-led coordinating body and provide recommendations for how stakeholders-including government data centres, museums and those who fund, permit and publish bio-logging work-can support these efforts.
This report presents an overview of data identification and documentation related to biodiversity, ecosystem services, and the associated drivers, pressures, and response mechanisms. While not systematic nor exhaustive, our effort of data identification and documentation allowed us to describe more than 100 datasets and databases on European biodiversity (most datasets), ecosystem services, the drivers and pressures affecting them, and the mechanisms put in place to address these. These datasets represent nearly 2000 variables and metrics that can be used directly by researchers, land managers and decision-makers, for example for spatial planning in conservation or for further integration into biodiversity and ecosystem services models.
Moreover, we also evaluate the completeness of biodiversity data (occurrence, trait and biotic interactions) in Europe across four terrestrial vertebrate classes, and assess potential drivers of data completeness. Despite Europe being one of the richest continents for biodiversity data globally, there are substantial data gaps in species distribution, trait, and species interactions, particularly in Eastern Europe, and for reptiles and amphibians. Results highlight how this heterogeneity in data availability is strongly associated with socioeconomic factors.
We found that freshwater systems, data on ecosystem functions and population abundances are overall still under-represented in large-scale biodiversity data repositories and catalogues such as the ones that we consulted to build our metadatabase. In contrast, most of the metrics identified can be classified as species traits (both functional and life-history traits) although those also largely related to static data. By design, most of the datasets that we describe are openly available and easily accessible. Nevertheless, they also vary greatly in formats and standardization efforts which would impair a smooth integration into open workflows that could support the wider adoption of the tools that projects such as NaturaConnect could develop.
Moreover, knowledge gaps are unevenly distributed within the European continent showing a strong taxonomic but also geographic bias. Amphibian and reptile data are strongly under-sampled compared to mammals and birds considering the species distribution (Wallacean shortfall), biological traits (Raunkiæran shortfall), and trophic interactions data (Eltonian shortfall).
Some general recommendations in the view of these results are: i) there is a need to promote the publication of open protocols that describe in a standardized way the inputs and outputs of models used for decision-making and research in biodiversity conservation and that would limit the risk for redundancy, overestimations and circularity when integrating several datasets from various sources and disciplines; and ii) priority areas for data collection are located in southern and eastern Europe, which are strongly under-sampled compared to central and northern Europe (e.g., France, United Kingdom). Addressing these issues is crucial for advancing biodiversity conservation and ecosystem service management across Europe.
The concept of ecosystem services (ES) has greatly evolved since it was first proposed and, as it gained popularity, has been used in diverse applications. Today, ES are an important part of global and national environmental policies. In this context, there is a call for the monitoring of ES to support their management. The proliferation of terms used with the concept of ES may be a barrier to systematic monitoring. Monitoring ES requires knowing exactly what variables to measure and how they relate to change in the states of ES. It further requires interoperability between methodologies used by the information systems used to operationalise data flows. As such, there is a need to systematise the language used to define ES and the terminology used in their monitoring in a way that is unambiguous and both human and computer readable. Building on advances in other biological fields, we develop an ontology for monitoring ES. Ontologies are tools that operationalise concepts and the relationships among terms used to define them. An ontology further allows people and machines to use the terms consistently. The ES monitoring ontology aligns the language of ES with other ontologies in the biological sciences. We test the ES monitoring ontology with data from three ES in British Columbia, Canada, to highlight how it can enable information sharing and monitoring. We invite members of the ES community to join the effort of developing this ontology for ES so that can it contribute to the challenge of systematically monitoring change in social-ecological systems.
Freshwater crayfish are amongst the largest macroinvertebrates and play a keystone role in the ecosystems they occupy. Understanding the global distribution of these animals is often hindered due to a paucity of distributional data. Additionally, non-native crayfish introductions are becoming more frequent, which can cause severe environmental and economic impacts. Management decisions related to crayfish and their habitats require accurate, up-to-date distribution data and mapping tools. Such data are currently patchily distributed with limited accessibility and are rarely up-to-date. To address these challenges, we developed a versatile e-portal to host distributional data of freshwater crayfish and their pathogens (using Aphanomyces astaci, the causative agent of the crayfish plague, as the most prominent example). Populated with expert data and operating in near real-time, World of Crayfish™ is a living, publicly available database providing worldwide distributional data sourced by experts in the field. The database offers open access to the data through specialized standard geospatial services (Web Map Service, Web Feature Service) enabling users to view, embed, and download customizable outputs for various applications. The platform is designed to support technical enhancements in the future, with the potential to eventually incorporate various additional features. This tool serves as a step forward towards a modern era of conservation planning and management of freshwater biodiversity.
The failure to halt the global decline in biodiversity by 2020 contributedto the adoption of the ambitious Kunming-Montreal Global BiodiversityFramework, which includes transparency and responsibility as foundations.The Global Biodiversity Framework identifies the actions needed so thatsocieties are living in harmony with nature by 2050. To support the deliveryof this ambition, the transparency and responsibility mechanismsdefined in the Global Biodiversity Framework include a detailedMonitoring Framework designed to prompt evidence-based actions andtrack progress towards its goals and targets at the national and globallevel. The Monitoring Framework includes a set of indicators selectedby the Parties through a political process. These indicators have sincebeen operationalized through a scientific process led by an expert groupfocused on assessing and clarifying their methods. Most indicators arenow ready to inform on progress, but key limitations of data availabilityand methodological challenges remain. The onus is now on the Partiesto resource implementation and on the scientific community to supportindicator use and development. Implementation of the MonitoringFramework will provide an unprecedented view of the state of biodiversityat the national level, which can be used to assess both national and globalprogress. Investment to overcome the Monitoring Framework’s weaknesseswill improve our ability to measure progress and mobilize the actionsneeded to protect and restore biodiversity and the many benefits we receivefrom nature.
Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model— Deepbiosphere— not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data, Deepbiosphere can detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.
Restoring biodiversity‐based resilience and ecosystem multi‐functionality needs to be informed by more accurate predictions of animal biodiversity responses to environmental change. Ecological models make a substantial contribution to this understanding, especially when they encode the biological mechanisms and processes that give rise to emergent patterns (population, community, ecosystem properties and dynamics). Here, a distinction between ‘mechanistic’ and ‘process‐based’ ecological models is established to review existing approaches. Mechanistic and process‐based ecological models have made key advances to understanding the structure, function and dynamics of animal biodiversity, but are typically designed to account for specific levels of biological organisation and spatiotemporal scales. Cross‐scale ecological models, which predict emergent co‐occurring biodiversity patterns at interacting scales of space, time and biological organisation, is a critical next step in predictive ecology. A way forward is to first capitalise on existing models to systematically evaluate the ability of scale‐explicit mechanisms and processes to predict emergent patterns at alternative scales. Such model intercomparisons will reveal mechanism to process transitions across fine to broad scales, overcome approach‐specific barriers to model realism or tractability and identify gaps which necessitate the development of new fundamental principles. Key challenges surrounding model complexity and uncertainty would need to be addressed, and while opportunities from big data can streamline the integration of multiple scale‐explicit biodiversity patterns, ambitious cross‐scale field studies are also needed. Crucially, overcoming cross‐scale ecological modelling challenges would unite disparate fields of ecology with the common goal of improving the evidence‐base to safeguard biodiversity and ecosystems under novel environmental change.
The causes of biodiversity change are of great scientific interest and central to policy efforts aimed at meeting biodiversity targets. Changes in species diversity and high rates of compositional turnover have been reported worldwide. In many cases, trends in biodiversity are detected, but these trends are rarely causally attributed to possible drivers. A formal framework and guidelines for the detection and attribution of biodiversity change is needed. We propose an inferential framework to guide detection and attribution analyses, which identifies five steps—causal modelling, observation, estimation, detection and attribution—for robust attribution. This workflow provides evidence of biodiversity change in relation to hypothesized impacts of multiple potential drivers and can eliminate putative drivers from contention. The framework encourages a formal and reproducible statement of confidence about the role of drivers after robust methods for trend detection and attribution have been deployed. Confidence in trend attribution requires that data and analyses used in all steps of the framework follow best practices reducing uncertainty at each step. We illustrate these steps with examples. This framework could strengthen the bridge between biodiversity science and policy and support effective actions to halt biodiversity loss and the impacts this has on ecosystems.
This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’.
Despite decades of increasing investment in conservation, we have not succeeded in ''bending the curve'' of biodiversity decline. Efforts to meet new targets and goals for the next three decades risk repeating this outcome due to three factors: neglect of increasing drivers of decline; unrealistic expectations and time frames of biodiversity recovery; and insufficient attention to justice within and between generations and across countries. Our Earth system justice approach identifies six sets of actions that when tackled simultaneously address these failings: (1) reduce and reverse direct and indirect drivers causing decline; (2) halt and reverse biodiversity loss; (3) restore and regenerate biodiversity to a safe state; (4) raise minimum wellbeing for all; (5) eliminate over-consumption and excesses associated with accumulation of capital; and (6) uphold and respect the rights and responsibilities of all communities, present and future. Current conservation campaigns primarily address actions 2 and 3, with urgent upscaling of actions 1, 4, 5, and 6 needed to help deliver the post-2020 global biodiversity framework.
Spatial patterns of biodiversity are inextricably linked to their collection methods, yet no synthesis of bias patterns or their consequences exists. As such, views of organismal distribution and the ecosystems they make up may be incorrect, undermining countless ecological and evolutionary studies. Using 742 million records of 374 900 species, we explore the global patterns and impacts of biases related to taxonomy, accessibility, ecotype and data type across terrestrial and marine systems. Pervasive sampling and observation biases exist across animals, with only 6.74% of the globe sampled, and disproportionately poor tropical sampling. High elevations and deep seas are particularly unknown. Over 50% of records in most groups account for under 2% of species and citizen‐science only exacerbates biases. Additional data will be needed to overcome many of these biases, but we must increasingly value data publication to bridge this gap and better represent species' distributions from more distant and inaccessible areas, and provide the necessary basis for conservation and management.
As big data, open data, and open science advance to increase access to complex and large datasets for innovation, discovery, and decision-making, Indigenous Peoples’ rights to control and access their data within these data environments remain limited. Operationalizing the FAIR Principles for scientific data with the CARE Principles for Indigenous Data Governance enhances machine actionability and brings people and purpose to the fore to resolve Indigenous Peoples’ rights to and interests in their data across the data lifecycle.
Despite conservation commitments, most countries still lack large-scale biodiversity monitoring programs to track progress toward agreed targets. Monitoring program design is frequently approached from a top-down, data-centric perspective that ignores the socio-cultural context of data collection. A rich landscape of people and organizations, with a diversity of motivations and expertise, independently engages in biodiversity monitoring. This diversity often leads to complementarity in activities across places, time periods, and taxa. In this Perspective, we propose a framework for aligning different efforts to realize large-scale biodiversity monitoring through a networked design of stakeholders, data, and biodiversity schemes. We emphasize the value of integrating independent biodiversity observations in conjunction with a backbone of structured core monitoring, thereby fostering broad ownership and resilience due to a strong partnership of science, society, policy, and individuals. Furthermore, we identify stakeholder-specific barriers and incentives to foster joint collaboration toward effective large-scale biodiversity monitoring.
1. There is a global need for observation systems that deliver regular, timely data on state and trends in biodiversity, but few have been implemented, and fewer still at national scales. We describe the implementation of measurement of Essential Biodiversity Variables (EBVs) on an 8 km × 8 km grid throughout New Zealand, with multiple components of biodiversity (vegetation, birds, and some introduced mammals) measured simultaneously at each sample point.
2. Between 2011 and 2017, all public land was sampled nationally (ca. 1,350 points) and some private land (ca. 500 points). Synthetic appraisals of the state of New Zealand's biodiversity, not possible previously, can be derived from the first measurement of species distribution, population abundance, and taxonomic diversity EBVs.
3. Native bird counts (all species combined) were about 2.5 times greater per sample point in natural forests and shrublands than in non‐woody ecosystems, and native bird counts exceeded those of non‐native birds across all natural forests and shrublands.
4. Non‐native plants, birds, and mammals are invasive throughout, but high‐rainfall forested regions are least invaded, and historically deforested rain shadow regions are most invaded.
5. National reporting of terrestrial biodiversity across New Zealand's public land is established and becoming normalised, in the same manner as national and international reporting of human health and education statistics. The challenge is extending coverage across all private land. Repeated measurements of these EBVs, which began in 2017, will allow defensible estimates of biodiversity trends.
Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.
Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).
Time period and grain: BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.
Software format: .csv and .SQL.
The ability to monitor changes in biodiversity, and their societal impact, is critical to conserving species and managing ecosystems. While emerging technologies increase the breadth and reach of data acquisition, monitoring efforts are still spatially and temporally fragmented, and taxonomically biased. Appropriate long-term information remains therefore limited. The Group on Earth Observations Biodiversity Observation Network (GEO BON) aims to provide a general framework for biodiversity monitoring to support decision-makers. Here, we discuss the coordinated observing system adopted by GEO BON, and review challenges and advances in its implementation, focusing on two interconnected core components — the Essential Biodiversity Variables as a standard framework for biodiversity monitoring, and the Biodiversity Observation Networks that support harmonized observation systems — while highlighting their societal relevance.
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, highthroughput 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.
Key global indicators of biodiversity decline, such as the IUCN Red List Index and the Living Planet Index, have relatively long assessment intervals. This means they, due to their inherent structure, function as late-warning indicators that are retrospective, rather than prospective. These indicators are unquestionably important in providing information for biodiversity conservation, but the detection of early-warning signs of critical biodiversity change is also needed so that proactive management responses can be enacted promptly where required. Generally, biodiversity conservation has dealt poorly with the scattered distribution of necessary detailed information, and needs to find a solution to assemble, harmonize and standardize the data. The prospect of monitoring essential biodiversity variables (EBVs) has been suggested in response to this challenge. The concept has generated much attention, but the EBVs themselves are still in development due to the complexity of the task, the limited resources available, and a lack of long-term commitment to maintain EBV data sets. As a first step, the scientific community and the policy sphere should agree on a set of priority candidate EBVs to be developed within the coming years to advance both large-scale ecological 2 Dirk S. Schmeller and others research as well as global and regional biodiversity conservation. Critical ecological transitions are of high importance from both a scientific as well as from a conservation policy point of view, as they can lead to long-lasting biodiversity change with a high potential for deleterious effects on whole ecosystems and therefore also on human well-being. We evaluated candidate EBVs using six criteria: relevance, sensitivity to change, generalizability, scalability, feasibility, and data availability and provide a literature-based review for eight EBVs with high sensitivity to change. The proposed suite of EBVs comprises abundance, allelic diversity, body mass index, ecosystem heterogeneity, phenology, range dynamics, size at first reproduction, and survival rates. The eight candidate EBVs provide for the early detection of critical and potentially long-lasting biodiversity change and should be operationalized as a priority. Only with such an approach can science predict the future status of global biodiversity with high certainty and set up the appropriate conservation measures early and efficiently. Importantly, the selected EBVs would address a large range of conservation issues and contribute to a total of 15 of the 20 Aichi targets and are, hence, of high biological relevance.
The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.
Reducing the rate of biodiversity loss and averting dangerous biodiversity change are international goals, reasserted by the Aichi Targets for 2020 by Parties to the United Nations (UN) Convention on Biological Diversity (CBD) after failure to meet the 2010 target ( 1, 2). However, there is no global, harmonized observation system for delivering regular, timely data on biodiversity change ( 3). With the first plenary meeting of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) soon under way, partners from the Group on Earth Observations Biodiversity Observation Network (GEO BON) ( 4) are developing—and seeking consensus around—Essential Biodiversity Variables (EBVs) that could form the basis of monitoring programs worldwide.
The Group on Earth Observations Biodiversity Observation Network (GEO BON) has been in formal existence for three years, following several years of design and discussion. It is the realisation of the biodiversity societal benefit area envisaged in the GEO System of Systems (GEOSS). GEO BON links together existing networks, each covering particular aspects of biodiversity or parts of the world, and takes steps to help fill important gaps in the system. GEO BON focusses on coordination and harmonisation of the existing and emerging systems; advocacy and action to sustain the observing systems and to fill the identified gaps; and understanding and servicing user needs for biodiversity observations, particularly in the policy-making domain.
This week, Montreal, Canada, is at the epicenter of international negotiations for biodiversity. Thousands of people from around the world are attending the 15th Conference of the Parties to the United Nations Convention on Biological Diversity (COP15) to witness the negotiation of a new Global Biodiversity Framework. Its goals and targets replace the previous framework—the Aichi Biodiversity Targets—that failed to bring about the transformative change needed to reverse the alarming trends in biodiversity loss.
Governments are negotiating actions intended to halt biodiversity loss and put it on a path to recovery by 2050. Here, we show that bending the curve for biodiversity is possible, but only if actions are implemented urgently and in an integrated manner. Connecting these actions to biodiversity outcomes and tracking progress remain a challenge.
Remote sensing has transformed the monitoring of life on Earth by revealing spatial and temporal dimensions of biological diversity through structural, compositional and functional measurements of ecosystems. Yet, many aspects of Earth’s biodiversity are not directly quantified by reflected or emitted photons. Inclusive integration of remote sensing with field-based ecology and evolution is needed to fully understand and preserve Earth’s biodiversity. In this Perspective, we argue that multiple data types are necessary for almost all draft targets set by the Convention on Biological Diversity. We examine five key topics in biodiversity science that can be advanced by integrating remote sensing with in situ data collection from field sampling, experiments and laboratory studies to benefit conservation. Lowering the barriers for bringing these approaches together will require global-scale collaboration.
Global frameworks to guide consistent monitoring of changes in human–nature interactions across space and time are needed to better understand how healthy ecosystems support societies and to inform policy design. Monitoring Essential Ecosystem Service Variables (EESVs) can provide a comprehensive picture of how links between nature and people are changing. A first proposed set of EESV classes comprises: ecological supply, anthropogenic contribution, demand, use, instrumental values, and relational values. Development of specific indicators of these classes for three exemplary ecosystem services (food from fisheries, crop pollination and wildlife viewing) confirms their readiness for global operationalization. The EESV classes will advance our ability to monitor progress towards achieving the Sustainable Development Goals.
BACKGROUND
As global climate change accelerates, one of the most urgent tasks for the coming decades is to develop accurate predictions about biological responses to guide the effective protection of biodiversity. Predictive models in biology provide a means for scientists to project changes to species and ecosystems in response to disturbances such as climate change. Most current predictive models, however, exclude important biological mechanisms such as demography, dispersal, evolution, and species interactions. These biological mechanisms have been shown to be important in mediating past and present responses to climate change. Thus, current modeling efforts do not provide sufficiently accurate predictions. Despite the many complexities involved, biologists are rapidly developing tools that include the key biological processes needed to improve predictive accuracy. The biggest obstacle to applying these more realistic models is that the data needed to inform them are almost always missing. We suggest ways to fill this growing gap between model sophistication and information to predict and prevent the most damaging aspects of climate change for life on Earth.
ADVANCES
On the basis of empirical and theoretical evidence, we identify six biological mechanisms that commonly shape responses to climate change yet are too often missing from current predictive models: physiology; demography, life history, and phenology; species interactions; evolutionary potential and population differentiation; dispersal, colonization, and range dynamics; and responses to environmental variation. We prioritize the types of information needed to inform each of these mechanisms and suggest proxies for data that are missing or difficult to collect. We show that even for well-studied species, we often lack critical information that would be necessary to apply more realistic, mechanistic models. Consequently, data limitations likely override the potential gains in accuracy of more realistic models. Given the enormous challenge of collecting this detailed information on millions of species around the world, we highlight practical methods that promote the greatest gains in predictive accuracy. Trait-based approaches leverage sparse data to make more general inferences about unstudied species. Targeting species with high climate sensitivity and disproportionate ecological impact can yield important insights about future ecosystem change. Adaptive modeling schemes provide a means to target the most important data while simultaneously improving predictive accuracy.
OUTLOOK
Strategic collections of essential biological information will allow us to build generalizable insights that inform our broader ability to anticipate species’ responses to climate change and other human-caused disturbances. By increasing accuracy and making uncertainties explicit, scientists can deliver improved projections for biodiversity under climate change together with characterizations of uncertainty to support more informed decisions by policymakers and land managers. Toward this end, a globally coordinated effort to fill data gaps in advance of the growing climate-fueled biodiversity crisis offers substantial advantages in efficiency, coverage, and accuracy. Biologists can take advantage of the lessons learned from the Intergovernmental Panel on Climate Change’s development, coordination, and integration of climate change projections. Climate and weather projections were greatly improved by incorporating important mechanisms and testing predictions against global weather station data. Biology can do the same. We need to adopt this meteorological approach to predicting biological responses to climate change to enhance our ability to mitigate future changes to global biodiversity and the services it provides to humans.
Emerging models are beginning to incorporate six key biological mechanisms that can improve predictions of biological responses to climate change
Models that include biological mechanisms have been used to project (clockwise from top) the evolution of disease-harboring mosquitoes, future environments and land use, physiological responses of invasive species such as cane toads, demographic responses of penguins to future climates, climate-dependent dispersal behavior in butterflies, and mismatched interactions between butterflies and their host plants. Despite these modeling advances, we seldom have the detailed data needed to build these models, necessitating new efforts to collect the relevant data to parameterize more biologically realistic predictive models.
Governments have set the ambitious target of reducing biodiversity loss by the year 2010. The scientific community now faces the challenge of assessing the progress made towards this target and beyond. Here, we review current monitoring efforts and propose a global biodiversity monitoring network to complement and enhance these efforts. The network would develop a global sampling programme for indicator taxa (we suggest birds and vascular plants) and would integrate regional sampling programmes for taxa that are locally relevant to the monitoring of biodiversity change. The network would also promote the development of comparable maps of global land cover at regular time intervals. The extent and condition of specific habitat types, such as wetlands and coral reefs, would be monitored based on regional programmes. The data would then be integrated with other environmental and socioeconomic indicators to design responses to reduce biodiversity loss.
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