In this session, we aim to highlight case studies that have applied innovative predictive models to facilitate the transformation of conservation ambitions into effective actions, with an emphasis on examples with demonstrated policy relevance. Examples might include population models that suggest the best mitigation methods to prevent the extinction of endangered species, models supporting corridor placement between protected areas, national to global models that indicate priority regions and species for conservation under varying policy-relevant scenarios, and novel cutting-edge mechanistic models such as supporting adaptive evolution to environmental change.
Overall, our session will demonstrate the value of predictive models in facilitating actions that best support biodiversity protection and facilitate discussions among policymakers, conservation practitioners, scientists, and modelers. The session would be supported by the GEO BON EcoCode collaborative. We foresee the development of a high-profile journal article summarizing and synthesizing case studies and a companion white paper for the UNEP for distribution to GBF parties.
Predictive biodiversity models are emerging as essential tools to translate global, regional, and national biodiversity strategies into effective actions. By explicitly linking conservation measures to expected ecological outcomes, these models allow us to evaluate alternative policy pathways, anticipate biodiversity responses to different management strategies, reveal trade-offs such as balancing habitat protection with agricultural needs, and ensure that conservation actions are effective, efficient, and equitable. As many conservation actions can take years or even decades to unfold, forward-looking models are essential to anticipate whether current actions will suffice to meet biodiversity targets. Yet their potential remains underused in planning and policy processes worldwide. In this talk, we briefly summarise recent advances in predictive biodiversity modelling and illustrate how models can help bridge the gap between biodiversity goals and action. We draw from a wide range of examples, such as uncertainty-aware “blacklisting’’ of invasive plants that supports prevention and early-detection strategies; assessments of EU agricultural and biodiversity measures and their effectiveness for maintaining farmland bird populations; model-based analyses of restoration success and genetic connectivity in recovering bison populations; and global and regional biodiversity model intercomparisons that help identify which ecological processes most strongly drive biodiversity change. Together, these case studies show how predictive approaches can reveal hidden trade-offs, quantify uncertainties, and fill key knowledge gaps highlighted by recent global assessments. We also emphasise the importance of easy-to-use modelling toolboxes, participatory approaches that engage stakeholders and policy makers throughout the modelling process, and capacity-building initiatives that enable broader adoption and co-development of predictive tools worldwide. These elements increase transparency and mutual understanding, and ensure that modelling outputs are tailored to real-world decision needs. Taken together, our examples demonstrate how predictive modelling can help move biodiversity science from diagnosis toward solution, supporting the design and evaluation of strategies that are scientifically robust, societally relevant, and capable of guiding long-term conservation success.
How to cite: Zurell, D., Bocedi, G., Velazco, S., and Urban, M.: Predicting the way toward Nature’s recovery, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-261, https://doi.org/10.5194/wbf2026-261, 2026.
Halting and reversing species declines is key to reaching global biodiversity targets, yet ineffective interventions continue due to a lack of rigorous, evidence-based evaluation. Multifactorial drivers and data limitations often hinder conservation planning. Predictive modelling approaches that integrate diverse data sources with biological hypotheses can bridge this gap by clarifying decline drivers and evaluating management options under uncertainty.
We developed a Bayesian integrated population model (IPM) applicable to lekking species, combining incomplete data from different life stages and seasons while accounting for observation error. IPMs are widely recognised for improving and reducing bias compared to single-data-stream analyses, yet their application in conservation planning remains limited. We applied the model to the threatened Western Capercaillie population in Scotland which has been declining since at least the 1980s despite decades of concerted conservation efforts, a typical case requiring urgent and evidence-based management.
By fitting the model to 30 years of population data, we investigated associations between demographic processes and their potential drivers, including weather variables, fence collision mortality and a proxy of predation pressure. Data integration improved population estimate precision by 17-50% relative to standalone national survey estimates, confirming that the decline that started in the 1980s continued from 1990-2023. Breeding success was related to the pattern of April warming, negatively affected by pre-breeding precipitation and positively by vole abundance, the latter consistent with the alternative prey hypothesis.
We use model predictions to evaluate the joint effectiveness of proposed management actions aimed at improving vital rates, including fence management and diversionary feeding. We project extinction risk and potential population trajectories under management scenarios to inform evidence-based recommendations for conservation. This modelling approach forms a key component of the Capercaillie Emergency Plan 2025-2030 and could provide a platform for future adaptive management, enabling iterative evaluation of actions as new data become available. Beyond this case study, our integrated approach offers a transferable framework for managing multifactorial declines in other threatened species, supporting biodiversity targets under uncertainty.
How to cite: Niven, H. I., Blyth, F., Bamber, J. A., Doubleday, M., Ewing, S. R., Fletcher, K., Haysom, S., Kortland, K., Lambin, X., Moss, R., Sutherland, C., Zantis, L. J., and Matthiopoulos, J.: Applying integrated population models to guide conservation planning: Western Capercaillie in Scotland, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-52, https://doi.org/10.5194/wbf2026-52, 2026.
Climate-driven shifts in marine biodiversity are reshaping ecosystems globally, yet most projections focus on charismatic or commercially important species. This leaves major gaps in understanding how climate change affects whole food webs, particularly through changes in rare species that can disproportionately influence biodiversity and ecosystem resilience. We address this gap by providing the first multidimensional projections of climate-driven biodiversity change across co-occurring phytoplankton, zooplankton, benthos and fish assemblages in the Northeast Atlantic. Using Hill numbers to quantify α-, β-, and γ-diversity, we systematically capture changes in richness, composition, and rarity across trophic levels. Leveraging extensive long-term survey data and Bayesian Additive Regression Trees, we project biodiversity trajectories to 2100 under multiple emissions scenarios, with detailed results shown for RCP 4.5.
Our projections reveal divergent responses across the marine food web. Fish and benthos show widespread increases in γ-diversity driven by poleward range expansion, whereas phytoplankton and zooplankton exhibit widespread declines, indicating potential disruptions to energy transfer from lower to higher trophic levels. By comparing Hill numbers of different orders, we show that shifts in rare species are central to these patterns: climate-driven arrivals of newcomers increase biodiversity in some regions, while the loss of locally rare species signals decline in others. These dynamics cannot be captured by conventional species distribution models focused on individual taxa.
Together, these results demonstrate that climate change will restructure marine biodiversity in ways that vary markedly across trophic levels. To support management under shifting baselines, our work provides a suite of complementary biodiversity indicators capable of assessing current status and projecting future trends across assemblages. These indicators provide early warning of biodiversity change and offer a pathway toward embedding predictive, preventive tools into decision-making and long-term conservation strategies. Our framework identifies emerging hotspots of biodiversity change, highlights regions at risk of food-web disruption, and contributes a scalable approach for tracking progress toward international biodiversity targets in a rapidly warming ocean.
How to cite: Greig, L., Couce, E., Thompson, M., Engelhard, G., Pinnegar, J., Cooper, K., Hélaouët, P., Pecuchet, L., Peck, M., and Lindegren, M.: From plankton to fish: 21st-century redistribution of marine biodiversity and the changing role of rare species, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-303, https://doi.org/10.5194/wbf2026-303, 2026.
Boreal and tropical forests underpin a wide range of critical Earth system functions, including long-term carbon storage, regional and global climate regulation, hydrological stability, and the maintenance of habitat for countless species. Because these ecosystems contribute so fundamentally to planetary resilience, their preservation is essential for maintaining a safe operating space for humanity in the face of accelerating environmental change. Biodiversity plays a central role in sustaining these functions, yet ongoing climate warming, increasing climate variability, and rapidly shifting land-use patterns are driving widespread losses of species and ecological interactions. Current global assessments suggest that tropical forests, in particular, are experiencing persistent biodiversity declines due to deforestation, fragmentation, and climatic stressors. However, scenarios that assume stronger sustainability policies, such as the Shared Socioeconomic Pathway SSP1 paired with the low-emissions RCP2.6 trajectory, indicate that improved land-use management and reduced pressures may help alleviate some of these losses. Under this same scenario, boreal regions are projected to experience a more moderate decline in species richness, though responses remain spatially variable and highly dependent on regional climate impacts.
Despite these insights, many biodiversity assessments still rely on modeling approaches that overlook fundamental ecological processes, especially demographic dynamics and dispersal capacities that determine long-term metapopulation viability and ecosystem functionality. To address this gap, we apply a novel multispecies modeling framework, MetaRange, which integrates demographic rates and dispersal processes into traditional habitat-suitability-based projections. Using several key functional groups of mammals as focal taxa, we simulate potential biodiversity trajectories under the SSP1–RCP2.6 scenario to evaluate the feasibility of achieving a nature-positive future, defined here as maintaining or increasing population abundance relative to a 2015 baseline by the year 2100. Our results reveal substantial differences among functional groups, with spatially heterogeneous patterns of gains and losses driven by complex metapopulation dynamics. Ultimately, our approach demonstrates the feasibility and value of large-scale mechanistic simulations for advancing global change research and informing conservation strategies.
How to cite: Pinto da Silva, A., Silva, M. I., Rieger, J.-M., Fallert, S., and Rocha, J.: Biodiversity future in critical earth system forests under a green growth socio-economic pathway, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-399, https://doi.org/10.5194/wbf2026-399, 2026.
Global biodiversity targets can be tackled through a variety of policy and implementation pathways. Understanding the potential implications of these alternate strategies is vital to navigating the tensions and synergies between biodiversity, land use, climate and socio-economic factors. The Nature Futures Framework (NFF) facilitates the development of scenarios that differ in how nature is valued – how the human-nature relationship is perceived. Whether we value nature for nature’s sake, for its utility to humans or for its cultural significance influences how land systems are managed towards achieving the 2030 targets of the Kunming-Montreal Global Biodiversity Framework (KM-GBF). For instance, where protected areas are established, how strictly they are managed, and the extent to which land-sharing versus land-sparing approaches are adopted when implementing targets 1–3 on conservation and restoration. Such decisions directly affect the biodiversity these systems can sustain, particularly given that land-use change is the predominant driver of terrestrial biodiversity shifts globally. Because biodiversity is at the core of the framework, the expectation is that NFF scenarios will be more nature-positive than those developed under the Shared Socioeconomic Pathways. However, so far, there have been few global biodiversity projections under alternative NFF scenarios. Here, we present a modelling framework that couples the PREDICTS database with the PLUM land-use model to produce global, 1-km resolution projections of the Biodiversity Intactness Index (BII) under four NFF scenarios and a business-as-usual future. We show that these scenarios differ markedly from each other. Some regions exhibit far greater variability between scenarios than others, highlighting where policy choices may have the greatest impact for biodiversity. We also identify unintended consequences of interventions when implemented within broader socio-economic contexts, such as the displacement of timber extraction to previously unmanaged, but unprotected forests, when protected areas are managed strictly in a less globalised world. Across all NFF scenarios, achieving the 2030 targets of the KM-GBF leads to short-term biodiversity gains. However, long-term trends still point toward continued decline. These findings suggest that while current targets can deliver near-term benefits, they may fall short of the ambition required to meet 2050 goals to halt and reverse biodiversity loss.
How to cite: Walkden, P., Burns, J., Contu, S., Duffin, C., Alexander, P., Rounsevell, M., McGinty, C., De Palma, A., Burgess, N., and Purvis, A.: Modelling the paths ahead: Predicting future global biodiversity intactness under Nature Future Framework scenarios, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-176, https://doi.org/10.5194/wbf2026-176, 2026.
The degradation of climate and biodiversity are two major crises that humans are facing and for which rapid action is needed. Both crises are partially linked and susceptible to threaten nature’s contribution to peoples (NCPs) and thus human well-being. Hence, it is essential to find efficient strategies for protecting key areas for both biodiversity and NCPs for a sustainable future. Studies at various scales have already used species distributions and NCP maps to identify the most optimal areas for safeguarding both components. Yet most NCP maps still rely on land-cover indicators that overlook how biodiversity supports these contributions. We propose that species-level data, widely used in biodiversity mapping, can better inform direct NCP prediction through the development of Species’ Contributions to People (SCP). Linking species to NCP through relational SCP databases and combining these with species distribution models and global change scenarios can yield spatial NCP predictions that can better rooted in species’ ecological characteristics. This approach fosters integrated biodiversity–NCP planning and supports conservation goals. Yet, an evaluation of how changes in species distributions could affect NCPs was still lacking. Here, based on a recently established table of relationships between more than 2,000 native vertebrate and tracheophyte species and 17 NCPs, we propose and illustrate a novel approach to predict the spatial distribution of NCPs from individual species predictions for the current period and four future time-scenarios in the Western Swiss Alps. Predictions of the different NCPs and their categories show varying degrees of spatial correlation, with some NCPs revealing very distinct patterns across time-scenarios. Our study highlights the potential to predict NCPs directly from species predictions in biodiversity assessments, allowing a better understanding and a better anticipation of the way species contribute to NCP and human well-being. The species-based NCP prediction approach we propose constitutes a major new asset to improve spatial conservation planning, but the development of such species-NCP tables should continue, and larger databases be compiled. We outline key methodological steps, identify research needs, and encourage collective progress to advance species-NCP knowledge for a transformative shift in how we assess and manage biodiversity and NCP.
How to cite: Guisan, A., Rey, P.-L., and Adde, A.: From Species Distribution Models to Spatial Predictions of Nature’s Contributions to People, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-362, https://doi.org/10.5194/wbf2026-362, 2026.
Despite recent commitments to protect freshwater ecosystems globally, significant uncertainty remains about our ability to achieve protection goals to sustain the benefits rivers provide to people and nature. A major barrier to action is an incomplete understanding of the status of river protection – most existing assessments measure only where rivers overlap with terrestrial protected areas – and spatially limited evaluations of how these protections coincide with, or are misaligned from, freshwater biodiversity. As momentum builds to tackle the freshwater biodiversity crisis and meet national and global conservation targets, we face the urgent challenge of determining which rivers to prioritize for future protection and where to reinforce and safeguard existing protections. The goals of this presentation are threefold. First, the results of the Protected Rivers Assessment of the United States are presented – a comprehensive investigation of the regulatory frameworks, conservation policies, and management practices across federal, state, local, tribal, and private jurisdictions that aim to protect rivers, riparian zones, floodplains, and surrounding lands. Second, a novel class of scalable spatial stream network models is applied and coupled with fish community data from > 1 million fish observations across the United States, to forecast fish species abundance across > 7 million river kilometers and to evaluate spatial congruence with river protection status. Third, network-based prioritization algorithms are used to identify priority areas for safeguarding/strengthening existing and scaling new protections that: (1) reduce stranded protections, increase protection river lengths, and enhance watershed dendritic connectivity; (2) best capture the distributional core and range of threatened and endangered species and native species that are currently under-protected; and (3) represent fish biodiversity strongholds with respect to taxonomic and functional alpha- and beta-diversity. Results from this assessment seek to inform new conservation priorities by aiding decision-makers who currently lack the data and tools to evaluate and track progress toward stronger, more durable river protections for freshwater biodiversity.
How to cite: Olden, J.: Safeguarding, strengthening, and scaling river protections for freshwater biodiversity, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-518, https://doi.org/10.5194/wbf2026-518, 2026.
The shared socio-economic pathways (SSPs) define five scenarios that span a range of socioeconomic development and sustainability. Developed for use by the climate change modelling community and featured in United Nations (UN) Intergovernmental Panel on Climate Change (IPCC) assessment reports, they provide projections of country level human population and gross domestic product (GDP). The Nature Futures Framework (NFF) was developed by the UN Intergovernmental Panel on Biodiversity and Ecosystem Services (IPBES) and characterizes three biodiversity positive scenarios that prioritize nature for: people, culture or nature. Neither of these scenario frameworks provides storylines or quantitative drivers about fisheries and marine spatial planning for the oceans. To address this gap, the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP) developed the ocean system pathways (OSPs) that extend the shared socioeconomic pathways (SSPs) to include oceans. Within the sustainability focussed SSP1, we include the three Nature Futures Framework (NFF) scenarios. The ocean system pathways (OSPs) detail qualitative storylines for the shared socioeconomic pathways (SSPs) and the Nature Futures Framework (NFF) about the future of fisheries, marine conservation, and fish consumption and provide quantitative drivers for use in climate change and biodiversity modelling projections using marine ecosystem models. The drivers include projections of globally gridded fishing effort by fleet and marine protected area coverage. They also include country level demand for wild and farmed fish. FishMIP is designing ocean system pathway (OSP) biodiversity scenarios for alignment with terrestrial projections of biodiversity through the Biodiversity and Ecosystem Services Scenario-based Inter-Model comparison (BES-SIM). The ocean system pathways (OSPs) have also been designed to address the UN Food and Agriculture Organization (FAO) blue transformation initiative for sustainable fisheries management and food security. Finally, FishMIP ocean system pathways (OSPs) allow for comparison across sectors represented in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), including agriculture, terrestrial biodiversity, and water.
How to cite: Eddy, T.: Bridging biodiversity and climate change scenarios for the future ocean , World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-727, https://doi.org/10.5194/wbf2026-727, 2026.
As Europe advances major policy initiatives - including the Nature Restoration Law and the EU Biodiversity Strategy for 2030 - there is growing demand for biodiversity models that can directly support the design, targeting, and evaluation of restoration actions. This requires innovation beyond existing indicators (e.g. national farmland bird indicators), which have been instrumental for describing retrospective biodiversity trends and raising awareness of biodiversity declines. However, policymakers now require predictive, spatially explicit, ecologically grounded models that can identify where interventions should be prioritised, anticipate potential trade-offs, and assess the likely outcomes of alternative policy pathways. Meeting these needs calls for a new generation of biodiversity models that link local ecological processes to national and continental targets in a coherent and decision-focused way.
In this study, we develop and apply a flexible, large-scale modelling framework that synthesises tens of millions of bird abundance time-series across Europe. The framework couples spatially explicit population models with environmental, climatic, and land-use predictors, while accounting for temporal dependence, spatial structure, imperfect detection, and phylogenetic relatedness. This models integrates best-practice statistical theory and tools to operate coherently across continental spatial extents and hundreds of species, producing local-scale predictions through time to help shape policy decisions.
Analyses reveal highly heterogeneous population responses to climate warming, protected area status, and anthropogenic land-use change. These patterns suggest that conservation actions may produce trade-offs: interventions that facilitate recovery in some species may inadvertently accelerate declines in others. Understanding this heterogeneity is essential for designing targeted restoration actions, prioritising locations for investment, and assessing the feasibility of reaching policy-defined targets. We further demonstrate how this framework can be used to re-evaluate existing bird biodiversity indicators, including farmland bird indices and pan-European trend metrics, by linking local population responses to aggregated national and continental indicators. This enables more accurate tracking of progress towards restoration goals and supports scenario-based assessments of alternative policy pathways.
Overall, our work highlights how scalable predictive models can strengthen biodiversity decision-support systems - informing restoration planning, guiding conservation investments, and supporting cohesive national and EU-level efforts to meet biodiversity targets.
How to cite: Johnson, T. F.: Using Predictive Models to Explore Routes to Biodiversity Recovery, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-640, https://doi.org/10.5194/wbf2026-640, 2026.
Tree diversity is fundamental to the ecosystem resilience, yet tree species information remains insufficiently incorporated into global restoration strategies. The Global Tree Assessment, coordinated by Botanic Gardens Conservation International (BGCI) and the IUCN/SSC Global Tree Specialist Group, provides the first comprehensive conservation assessment for the world’s ~57,500 tree species, revealing that at least one-third are threatened with extinction. Leveraging this evidence base is essential for advancing KMGBF Target 2, which calls for restoring at least 30% of degraded ecosystems by 2030.
The GlobalTree Portal aggregates data from the Global Tree Assessment offering species-level, country-level and global views of distribution and threat status. The GlobalTree Portal highlights gaps in both in situ and ex situ conservation and supports conservation planning decisions around species selection, and monitoring. It helps address key barriers to implementing Target 2 identifying where restoration is most needed, which species to prioritize and which interventions are required. By making data transparent and accessible, the Portal supports evidence-based decisions, mobilizes collaboration and fosters accountability.
The Global Tree Assessment also identifies tree species whose survival depends on a single irreplaceable site, generating a list requiring urgent action. These data help countries determine where restoration yields the highest biodiversity return and where rapid intervention is needed to prevent loss.
To strengthen policy integration, the Global Strategy for Plant Conservation (GSPC) aligns plant-focused actions with KMGBF targets that countries can embed in NBSAPs, and national and regional conservation strategies. This ensures that plant - and tree - conservation actions are systematically incorporated into national planning and restoration frameworks, providing a coherent pathway for implementation.
We will present case studies to show how countries, organisations and individuals can use the GlobalTree Portal and the GSPC to guide species selection, direct funding, align restoration with national biodiversity strategies and strengthen collaboration with local partners (including ). Embedding the tree information within policy and planning processes offers a scalable pathway to accelerate progress toward KMGBF Target 2 while safeguarding irreplaceable tree diversity and the ecosystems they support.
How to cite: Rivers, M., Beech, E., Hudson, A., Coletto, C., and Cowell, C.: Data-Driven Restoration Policy: Applying Global Tree Diversity Evidence to Accelerate KMGBF Target 2 , World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-869, https://doi.org/10.5194/wbf2026-869, 2026.
Conservation science sits between rich but often abstract grand theory and a vast body of practice-based case studies, yet it lacks the mid-range theory needed to link the two. This deficit limits our ability to advance knowledge, generalise across cases, and anticipate when and why particular interventions will work, fail, or backfire.
We propose a workflow for building mid-range theory for conservation science using simple quantitative models grounded in a shared ontology of social–ecological systems, and illustrate it based on case studies. Using the Coupled Infrastructure Systems (CIS) Framework, we first delimit the focal conservation system and identify key feedbacks between ecological, social, institutional, and built infrastructures. We then: (1) derive causal loop diagrams focused on specific management-relevant feedbacks; (2) translate these into clearly defined variables and mathematical relationships; (3) connect them into simple simulation models; and (4) use experiments to refine hypotheses, clarify domains of applicability, and identify critical variables and indicators.
We illustrate this process with two conservation problems: human–wildlife conflict around protected areas, and the provision of biodiversity as a public good on privately owned forests under European Union policy. In both cases, the workflow sharpened definitions (e.g. of system boundaries and actors), revealed under-recognised social variables (such as tolerance for wildlife and social license), and clarified where existing grand theories are either too abstract or too narrow to guide decisions. Focusing on feedbacks rather than whole systems reduced complexity while preserving core dynamics, and the shared CIS-based ontology facilitated comparison across cases and the construction of archetypes.
We argue that such mid-range models can improve the parsimony, empirical tractability, and predictive potential of conservation theory, support comparative and archetype-based research, and provide a basis for testing conservation measures before costly implementation. Developing mid-range theory in this way is essential if conservation science is to move beyond repeated trial-and-error learning toward more cumulative, theory-informed action.
How to cite: Grêt-Regamey, A., Anderies, M., Cumming, G., and De Vos, A.: Improving mid-range theory for conservation science, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-247, https://doi.org/10.5194/wbf2026-247, 2026.
Australia is a land of contradictions. A mega-diverse nation with a unique biodiversity legacy of millions of years of continental isolation and tens of thousands of years of Indigenous land stewardship. Most Australian birds and plants, and more than 90% of Australia’s mammals, reptiles, and amphibians are endemic to Australia and therefore globally unique. But since British colonisation just 250 years ago, Australia has suffered the highest rate of modern biodiversity loss of any developed nation, due largely to dramatic land use change involving continental-scale destruction of habitats, and the subsequent introduction of invasive and highly damaging species. Australia’s unique ecology and pressing modern biodiversity crisis has led to the development of novel, often cutting-edge applications of model-based decision support tools, including spatial population dynamics models, species distribution models, mechanistic niche and micro-climate models, ecological state-and-transition models, spatial prioritisation and other decision theory models to support policy, management, planning, monitoring design and optimal allocation of scarce conservation funding. I will provide an overview of the use of biodiversity models in Australian policy design and evaluation, regulation, urban planning, reserve design, national and global biodiversity reporting, and legal challenges. While many examples exist of the constructive application of biodiversity models in real-world, often high-stakes decisions, the reality is that the Australian biodiversity crisis is accelerating, propelled by climate change and ongoing land-use pressures. A new way of using models to mobilise public sentiment and design systemic change is urgently needed. I will discuss the need and opportunity for biodiversity modellers to work as part of interdisciplinary teams to bring greater positive impact to the benefit of society and biodiversity. New ways of constructing and communicating socio-ecological models are needed to motivate, understand, predict and evaluate societal transitions, and map plausible pathways toward global goals for biodiversity, climate, and people. With globally significant biodiversity to protect and restore and pressing climate, land-use and social polarisation challenges, Australia needs innovation in applied biodiversity modelling and decision support, and new cohort of biodiversity modellers to rise to the challenge.
How to cite: Wintle, B.: Biodiversity models in Australian environmental policy, regulation, planning and management – a short history and opportunities for urgently needed impact., World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-213, https://doi.org/10.5194/wbf2026-213, 2026.
Study of the dynamics of planktonic biota in the Bico-do-Papagaio Study Area (AEBP), Tocantins-Araguaia Hydrographic Region, employing an integrative multidimensional statistical framework to decode complex ecosystem patterns. Was applied PERMANOVA, SIMPER, PCA, UMAP, Random Forest, RDA, and dbRDA methodologies, we identified the hydrological pulse (Flood versus Drought seasons) as the paramount driver of ecological organization, accounting for 62% of total variance in initial global PERMANOVA with 16 limnological parameters. Following rigorous Variance Inflation Factor assessment to eliminate multicollinearity artifacts, five redundant variables were excluded, ensuring all subsequent analyses leveraged 11 statistically robust predictors. SIMPER analysis pinpointed Silicon, Biochemical Oxygen Demand, and Euphotic Zone depth as primary determinants of dissimilarity between hydrological phases. The flood regime manifested as a turbid, nutrient-enriched environment with limited light penetration, whereas drought conditions exhibited transparent waters with enhanced photic zone depth and concentrated pollutants. Machine learning implementation via Random Forest algorithm identified Total Phosphorus, Ammonium, Euphotic Zone, and Nitrate as paramount non-linear predictors of environmental variance. Phytoplankton assemblages demonstrated marked functional succession: flood periods sustained elevated diversity dominated by MBFG-IV (mucilaginous colonies) and MBFG-V (turbulence-adapted diatoms), while drought intervals were characterized by MBFG-VII filamentous cyanobacteria proliferating to extreme densities of 3,198.40 ind mL⁻¹, signaling pronounced eutrophication pressure. Zooplankton communities responded concordantly, with r-selected rotifers and nauplii prevailing during floods versus K-selected copepods increasing during cyanobacteria-dominated droughts. dbRDA synthesized these relationships into a potent predictive model where seasonal forcing, nitrate availability, and spatial autocorrelation (PCNM1) collectively explained 52% of adjusted variance in plankton community structure. Nested PERMANOVA delineated anthropogenic impacts from urban effluents as statistically discernible but subsidiary to natural seasonal dynamics, contributing merely 8.8% to explained variance, though exhibiting marked intensification during drought-mediated dilution capacity reduction. This robust methodological triangulation validates the tropical flood pulse as the fundamental ecological architect, establishing a critically valuable predictive framework for monitoring and managing tropical freshwater ecosystems. The model's prognostic capacity regarding alternative stable states, turbid/nutritious/diverse versus clear/eutrophic/cyanobacteria-dominated, provides indispensable tools for adaptive management of tropical aquatic resources amid accelerating climate change and anthropogenic stress, highlighting crucial intervention windows during drought periods when ecosystem resilience is most compromised.
How to cite: E. F. B. Carvalho, A., N. Silva, N., H. S. Silva, J., M. Santos, W., N. Silva, J., S. Cabral, E., F. Silva, M., and A. C. Silva, M.: Predictive Model in tropical freshwater ecosystems: Dynamics of Planktonic Biota on the Tocantins-Araguaia Hydrographic Region - RHTA, Ecotonal region of the Brazilian Cerrado - Eastern Amazon, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-128, https://doi.org/10.5194/wbf2026-128, 2026.
Climate change is a major driver of biodiversity loss, altering species distributions and increasing extinction risk through shifts in suitable habitats and ecological interactions. Reliable predictions of these responses are essential for conservation planning, ecosystem service assessments, and climate adaptation strategies. Species distribution models (SDMs) project future species distributions by relating occurrence data to environmental predictors. Although SDMs increasingly include factors like topography, dispersal, productivity, and land use, climate-only modeling remains common for isolating climate effects. Here, we employ climate-based SDMs to predict species occurrence as climate conditions change, and integrate these results with land-use projections to evaluate how both climate and landscape suitability impact species distribution.
Maintaining interconnected landscapes is crucial for supporting ecological processes, including daily animal movements, dispersal from native habitats, gene flow, habitat recolonization, and range shifts driven by climate change. Because ecological processes and movement differ by species and type, determining priority areas for protection requires attention to specific connections, targeted processes, and relevant timescales. This study introduces a global predictive framework for terrestrial vertebrates that integrates changing climate and habitat suitability, and multi-directional connectivity under diverse socioeconomic-climate scenarios (SSP1-RCP2.6, SSP3-RCP7.0, and SSP5-RCP8.5).
Using Omniscape’s circuit-theory-based algorithm, we quantified normalized current flow for terrestrial vertebrates across historical (1981–2010) and future (2041–2070, 2071–2100) time periods. Our connectivity maps reveal areas where ecological flows are impeded, intensified, or channeled, enabling us to identify critical pinch-points whose loss could sever essential landscape connections. We aggregated species-level results to create global indicators, such as the Connectivity Change Index (Δ Normalized Current Flow), and identify hotspots where climate suitability may rise but connectivity declines, informing priorities for ecosystem protection (to maintain existing connectivity functions) or restoration (to create alternative routes and alleviate flow constrictions). Furthermore, this approach enables the integration of additional data to target habitats for species of concern.
By integrating climate, land use, and connectivity analyses at the global scale, our framework guides evidence-based, adaptive management strategies, highlighting regions where investment in ecological corridors and habitat restoration will most effectively conserve biodiversity amidst ongoing climate and land-use change.
How to cite: Guzman Torres, M., Karger, D. N., and Davin, E. L.: Global Connectivity under different Climate and Land-use futures: A Predictive Framework for Biodiversity Conservation, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-618, https://doi.org/10.5194/wbf2026-618, 2026.
Predicting biodiversity change and translating those insights into effective conservation actions remain central challenges for meeting global and national biodiversity targets. Species Distribution Models (SDMs) are widely used to anticipate shifts in species ranges, yet their outputs—typically probabilities of occurrence—often require binarization before they can be used in policy-relevant applications such as priority area selection, biodiversity indicators, or multispecies composition assessments. Conventional thresholding approaches are often heuristic, introducing substantial biases in community estimates, especially when datasets include numerous rare or imbalanced species distributions.
We present a new decision-driven binarization framework developed to enhance the policy relevance of SDM-based predictions by selecting multispecies assemblages that directly optimize targeted evaluation metrics. This formulation treats binarization as a decision problem, allowing predictions to be explicitly aligned with conservation objectives without any use of additional data for calibrating the binarization. In parallel, we introduce a computationally efficient alternative—the Set Size Expectation (SSE) method—which predicts assemblages based on expected biodiversity richness and scales effectively to national and global modelling efforts. Both approaches have demonstrated strong empirical performance against literature reference methods across diverse taxa, environmental contexts and wide gradients in evaluated species richness
By providing more accurate and objective multispecies presence–absence predictions, our framework strengthens the ability of predictive models to support forward-looking conservation strategies. By allowing decision-optimized multispecies predictions, it improves the translation of SDM outputs into actionable insights for biodiversity monitoring, scenario evaluation, and spatial planning—key components for achieving the goals set by global biodiversity agreements.
Building on these advances, our current research integrates the optimal binarization step into a sampling pipeline that generates realistic species assemblages capable of capturing fine-scale multimodal distributions but also measuring prediction uncertainties. This development offers a more nuanced and ecologically grounded representation of biodiversity patterns, further enhancing their utility for monitoring and decision-support in the face of accelerating environmental change.
How to cite: Gigot--Léandri, S., Mouillot, D., Joly, A., Munoz, F., and Servajean, M.: Enhancing Biodiversity Target Assessments with Decision-Optimized Multispecies Predictions, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-761, https://doi.org/10.5194/wbf2026-761, 2026.
