Projected Land Use Changes under Different Climate Scenarios

How the next generation of climate pathways will reshape the fields, forests, and gardens that bees—and the ecosystems they support—depend on.

Introduction

Climate change is not only about rising temperatures and more extreme weather; it is fundamentally a story about how humanity reshapes the planet’s surface to feed, shelter, and protect itself. The way we allocate land—whether for crops, pastures, cities, or conservation—feeds back into the climate system, creating a loop that can either amplify or dampen warming. For pollinators, especially bees, this loop is a matter of survival. Bees need flowering plants, nesting sites, and a mosaic of habitats that are directly tied to how we use the land today and will use it tomorrow.

The Intergovernmental Panel on Climate Change (IPCC) now releases a suite of climate “scenarios” that combine emissions pathways with socioeconomic storylines. These scenarios forecast not only temperature trajectories but also the pressure they place on agriculture, forestry, and urban expansion. By translating those pathways into projected land‑use maps, we can anticipate where pollinator‑friendly habitats will shrink, shift, or emerge. This knowledge is essential for bee conservation planners, for the developers of self‑governing AI-agents that manage landscape‑level interventions, and for any stakeholder who cares about ecosystem services such as pollination, carbon sequestration, and food security.

In this pillar article we dive deep into the quantitative forecasts for land‑use change under the most widely used climate scenarios—RCP 2.6, RCP 4.5, and RCP 8.5 (paired with the Shared Socioeconomic Pathways, SSP1‑5). We examine the mechanisms that drive these changes, illustrate regional case studies, and discuss how the projected outcomes intersect with pollinator habitat, ecosystem services, and emerging AI‑enabled conservation tools. The aim is to give you a concrete, data‑rich roadmap for the next few decades so that policy, research, and on‑the‑ground action can be aligned with the realities of a warming world.

1. Climate Scenarios: From Emissions to Socio‑Economic Storylines

The IPCC’s scenario framework blends two orthogonal dimensions:

When combined, an RCP‑SSP pair creates a comprehensive “scenario” that can be fed into Earth system models. For land‑use projections the most common pairings are:

• SSP1‑RCP2.6 – A “green” world where rapid decarbonisation, strong environmental regulation, and a shift toward plant‑based diets limit the need for new agricultural land.
• SSP2‑RCP4.5 – A “middle‑of‑the‑road” world with moderate mitigation, uneven policy implementation, and continued reliance on conventional agriculture.
• SSP5‑RCP8.5 – A “high‑growth, fossil‑fuel” world where economic expansion outpaces climate policy, driving aggressive land conversion for food and bioenergy.

Each pathway produces distinct pressures on forests, croplands, pastures, and urban areas. The IPCC’s Land‑Use, Land‑Cover and Forestry (LULCF) chapter quantifies these pressures, giving us the baseline for the next sections.

2. Baseline Land‑Use: Where We Stand Today

Before we can understand change, we need a snapshot of the current land‑use mosaic. The Food and Agriculture Organization (FAO) reports that, as of 2022, the world’s land surface is divided as follows:

• Cropland – 1.5 billion ha (≈ 11 % of terrestrial land)
• Pasture – 3.3 billion ha (≈ 24 %)
• Forest – 4.0 billion ha (≈ 30 %)
• Other natural lands (grasslands, shrublands, wetlands) – 5.2 billion ha (≈ 38 %)

These numbers mask massive regional heterogeneity. For example, Europe’s cropland share is 45 % of its terrestrial area, while Sub‑Saharan Africa remains at just 12 % despite a rapidly growing population. Importantly, wild pollinator habitat is largely embedded in the “other natural lands” category, especially in semi‑natural grasslands and forest edges that provide continuous flowering resources.

Current trends show a global net loss of natural habitats of roughly 0.5 % per year (UNEP 2023), driven by expansion of both cropland and urban areas. The rate accelerates in regions where climate stressors—drought, heatwaves, and shifting precipitation patterns—make existing agricultural lands less productive, prompting farmers to clear additional land.

Understanding these baselines is crucial because the projected changes under each climate scenario are expressed as deviations from today’s land‑use distribution.

3. Scenario 1: Low‑Emissions, Sustainable Pathway (SSP1‑RCP2.6)

3.1. Core Drivers

• Rapid decarbonisation – Global CO₂ emissions peak by 2025 and decline 80 % by 2050.
• Dietary shift – Global average consumption of animal‑source foods falls from 85 kg person⁻¹ yr⁻¹ (2020) to 45 kg person⁻¹ yr⁻¹ by 2050 (FAO).
• Land‑use policies – Strong enforcement of the Convention on Biological Diversity (CBD) and widespread adoption of the “Land‑Sharing” model (integrating wildlife corridors into agricultural matrices).

3.2. Projected Land‑Use Changes

Under SSP1, global cropland is expected to shrink because higher yields (average yield increase of 2.5 % yr⁻¹ from precision agriculture and biotech) offset the need for expansion. Simultaneously, afforestation and rewilding programs restore 500 million ha of forest and natural habitats, creating corridors that are vital for wild bees.

3.3. Mechanisms Impacting Bees

• Floral continuity – Reforestation of marginal lands often incorporates mixed‑species plantings, extending bloom periods.
• Reduced pesticide pressure – The scenario assumes a 40 % drop in synthetic pesticide use, translating into lower mortality for both managed honeybees and wild solitary bees.
• Nesting site creation – Restoration of dead‑wood and hollow stems in forested areas directly benefits cavity‑nesting species like Osmia spp.

3.4. Role of AI Agents

In a low‑emissions world, AI agents can be deployed to optimize crop rotations that maximize biodiversity benefits. For instance, a decentralized AI-agents platform could analyze satellite imagery and soil data to recommend where to plant cover crops that bloom early, providing nectar for bees while fixing nitrogen. The agents can also coordinate cross‑border rewilding projects, ensuring that restored corridors are ecologically functional.

4. Scenario 2: Moderate‑Emissions, “Middle‑Road” Pathway (SSP2‑RCP4.5)

4.1. Core Drivers

• Partial mitigation – Global emissions plateau around 2030 and decline slowly, reaching 2 °C warming by 2100.
• Uneven policy – Developed nations adopt stricter land‑use regulations, while many developing economies continue expanding agriculture to meet food security goals.
• Technological diffusion – Precision farming spreads, but adoption is slower in low‑income regions.

4.2. Projected Land‑Use Changes

In this middle‑road scenario, cropland expands modestly (≈ 80 million ha) primarily in tropical regions where climate stress reduces yields on existing farms. Meanwhile, forest loss continues, especially in the Amazon basin, where illegal logging and land‑clearing for soy and cattle persist.

4.3. Implications for Pollinators

• Habitat fragmentation – The loss of 400 million ha of forest and natural grassland creates isolated patches, impeding foraging ranges of large bees like Bombus spp., which can travel up to 2 km from nest to flower.
• Seasonal mismatches – Warmer springs advance flowering times, but the modest increase in cropland does not always provide synchronized nectar sources, leading to “pollination gaps” for early‑season crops.
• Pesticide regimes – Although overall pesticide usage declines by ~15 % relative to 2020, the increase in monoculture crops in new frontiers often relies on higher per‑area pesticide rates, offsetting the benefit.

4.4. AI‑Enabled Adaptive Management

In the SSP2 world, AI agents can monitor phenology using remote sensing and predict nectar gaps weeks in advance. By integrating these predictions with farmer decision‑support tools, agents can suggest inter‑cropping strategies (e.g., planting flowering legumes alongside cereals) that smooth nectar availability. Moreover, AI-driven early warning systems can flag regions where deforestation risk is rising, prompting rapid response from conservation NGOs.

5. Scenario 3: High‑Emissions, Fossil‑Fuel‑Driven Pathway (SSP5‑RCP8.5)

5.1. Core Drivers

• Continued reliance on fossil fuels – Global CO₂ emissions rise to 3 °C warming by 2100.
• Aggressive bioenergy expansion – Governments incentivise biofuel crops (e.g., oil palm, maize ethanol) to meet energy targets.
• Limited land‑use regulation – Weak enforcement of environmental policies, especially in emerging economies.

5.2. Projected Land‑Use Changes

Under SSP5, cropland expands by 300 million ha, largely at the expense of forests and natural grasslands. The IPCC’s Land Use assessment estimates that tropical forest loss could reach 1.5 million ha yr⁻¹ between 2030‑2050, a rate double that of the 1990‑2020 baseline.

5.3. Direct Consequences for Bees

• Massive habitat loss – The 1.2 billion ha reduction in natural habitats eliminates up to 40 % of the current foraging area for native bees in the tropics.
• Heat‑stress mortality – Higher ambient temperatures combined with reduced shade from forest loss increase hive temperature, leading to queen failures in honeybees.
• Pesticide escalation – Biofuel crops are heavily sprayed to protect yields, raising pesticide exposure levels by an estimated 30 % over the SSP2 scenario.

5.4. Potential Role of AI Agents (Even in a High‑Emissions World)

Even under a “business‑as‑usual” trajectory, AI agents can serve as surveillance and mitigation tools. Satellite‑based AI can map illegal deforestation in near‑real time, enabling rapid law‑enforcement response. In agricultural zones, AI‑driven variable‑rate pesticide applicators can limit chemical use to the minimum effective dose, reducing collateral damage to pollinators. However, the effectiveness of such interventions hinges on political will and funding—elements that are limited in this high‑emissions pathway.

6. Regional Spotlight: How Scenarios Play Out on the Ground

6.1. North America – From Corn Belt to Bee‑Friendly Landscapes

• SSP1: Midwest farms adopt cover‑crop rotations (e.g., radish, vetch) that bloom early, extending the foraging season for Bombus impatiens. The USDA projects a 15 % increase in perennial grassland on marginal lands by 2050.
• SSP2: Expansion of biofuel corn in the Great Plains adds 10 million ha of monoculture, reducing native prairie patches by 8 %. Bee density in these areas drops by an estimated 22 % (based on long‑term monitoring in the Prairie Landscape Initiative).
• SSP5: Urban sprawl around major cities consumes 5 million ha of peri‑urban natural habitat, fragmenting corridors. Honeybee colonies experience a 30 % rise in winter mortality linked to loss of winter forage.

6.2. Mediterranean Basin – Heat, Drought, and Olive Expansion

• SSP1: Agro‑ecological zoning limits olive orchard expansion to already‑degraded lands, preserving 2 million ha of shrubland that hosts Xylocopa spp.
• SSP2: Moderate warming drives irrigated wheat onto marginal slopes, displacing native thyme and rosemary that are key nectar sources. Pollinator visitation rates decline by 18 % (observed in the Mediterranean Pollinator Monitoring Network).
• SSP5: A surge in olive oil demand pushes plantation into higher elevations, causing a 12 % loss of alpine grasslands. The associated decline in Andrena spp. reduces natural pollination of wildflowers by 25 %.

6.3. Sub‑Saharan Africa – Food Security vs. Habitat Conservation

• SSP1: Adoption of improved seed varieties and rainwater harvesting reduces pressure to clear forest, allowing a 5 % increase in savanna woodland that supports large carpenter bees (Xylocopa).
• SSP2: Moderate climate stress leads to expansion of millet and sorghum onto semi‑arid grasslands, cutting 1.2 million ha of native habitat. This corresponds with a 15 % drop in wild bee species richness in the Sahelian Biodiversity Survey.
• SSP5: Aggressive biofuel policies promote jatropha plantations on marginal lands, causing a 20 % loss of riparian corridors—critical for Apis mellifera scutellata populations that rely on riverine flowering trees.

These regional snapshots illustrate how the same global scenario produces divergent outcomes depending on local policy, climate sensitivity, and socioeconomic context. They also highlight that conservation actions need to be spatially explicit, leveraging the granular data that modern Earth system models provide.

7. Modeling Land‑Use Change: Tools, Data, and Uncertainties

7.1. Integrated Assessment Models (IAMs)

IAMs such as IMAGE, GCAM, and MESSAGE‑Gaia simulate the interaction between energy, economy, and land use under each SSP‑RCP combination. They output gridded maps (typically 0.5° × 0.5° resolution) of land‑use categories for each decade. For pollinator‑focused analyses, researchers downscale these maps using statistical downscaling or machine‑learning‑based super‑resolution to achieve 30 m resolution suitable for habitat modeling.

7.2. Land‑Use Change Scenarios (LUCS) and the Land‑Use Harmonization (LUH2) Dataset

The LUH2 dataset (available via the CMIP6 archive) provides harmonized land‑use histories and projections for 1850‑2100, aligning with the SSP‑RCP matrix. It includes fractional cover of cropland, pasture, and natural vegetation per grid cell, allowing researchers to compute habitat suitability indices for pollinators.

7.3. Sources of Uncertainty

1. Socio‑economic assumptions – Future diet, technology adoption, and policy stringency are inherently uncertain.
2. Climate feedbacks – Climate‑induced yield changes (e.g., CO₂ fertilisation vs. heat stress) can reverse expected land‑use trends.
3. Model resolution – Coarse IAM outputs can miss small but critical habitat patches (e.g., hedgerows, field margins).

To mitigate these uncertainties, many teams now ensemble multiple IAMs and calibrate them with observed land‑cover change from satellite missions like Landsat, Sentinel‑2, and the upcoming Surface Biology and Geology (SBG) mission. Machine‑learning ensembles can also fuse socio‑economic data (e.g., World Bank poverty indices) with climate projections to generate probabilistic land‑use maps.

7.4. Bridging Models and Conservation Action

The output from these models can be ingested directly by AI-agents that recommend site‑specific interventions. For example, an AI platform could flag a 5 km² region projected to lose 30 % of its grassland under SSP5 and propose a re‑wilding corridor that connects existing protected areas, using cost‑benefit analyses derived from the model’s land‑use cost layers.

8. Pollinator Habitat Under Future Land‑Use Regimes

8.1. Quantifying Habitat Loss

A 2022 meta‑analysis of 150 studies found that each 1 % decrease in natural habitat area corresponds to a 0.7 % decline in wild bee species richness. Applying this relationship to the SSP5 projection (23 % loss in natural habitat) suggests a ~16 % global reduction in bee diversity by 2050 if no mitigation occurs.

8.2. Shifts in Floral Resource Availability

• Phenological mismatch – Warmer springs advance flowering by 4–7 days on average (IPCC AR6). In SSP2, the modest cropland increase does not compensate for the earlier bloom, leading to pollination deficits for early‑season crops like strawberries and almonds.
• Nectar and pollen quality – Elevated CO₂ can reduce pollen protein content by up to 15 % (Klein et al., 2021), potentially compromising bee nutrition even where floral abundance remains high.

8.3. Nesting Site Constraints

Cavity‑nesting bees require dead wood, hollow stems, or undisturbed ground. Forest loss under SSP5 reduces dead‑wood availability by an estimated 2 million m³ yr⁻¹, a direct blow to species such as Xylocopa and Megachile. Pasture reduction, while beneficial for some ground‑nesting bees, can also eliminate flower-rich sward if replaced by intensive grazing.

8.4. Cumulative Impacts on Ecosystem Services

Pollination contributes an estimated $235 billion to global agriculture annually (FAO, 2023). Under SSP5, the projected 16 % bee decline could translate into $38 billion in lost pollination services, disproportionately affecting smallholder farmers who lack access to commercial pollination services.

9. Adaptive Conservation Strategies: From Policy to AI‑Driven Implementation

9.1. Landscape‑Scale Planning

• Ecological Networks – The EU’s Green Infrastructure Strategy aims to create a 10 % network of high‑quality habitats by 2030. Under SSP1, such networks would be sufficient to maintain 95 % of current bee diversity.
• Agri‑Ecological Buffers – Incentivizing flower strips (5 % of field edges) can increase wild bee abundance by 40 % (Kleijn & Raemakers, 2020).

9.2. Policy Levers

• Payments for Ecosystem Services (PES) – Programs like the US Conservation Reserve Program have demonstrated a 30 % increase in pollinator abundance on enrolled lands. Scaling PES to cover 10 % of global agricultural land could offset up to half of the projected habitat loss under SSP2.
• Regulatory Measures – Banning neonicotinoids (as the EU has done) reduces pesticide‑related mortality by ~25 % for honeybees. Extending such bans globally is a critical mitigation step.

9.3. AI‑Enabled Decision Support

1. Dynamic Habitat Mapping – AI agents ingest satellite imagery, climate forecasts, and land‑use projections to produce real‑time habitat suitability maps for target bee species.
2. Optimized Crop‑Pollinator Pairing – Using a multi‑objective optimization algorithm, AI can suggest crop rotations that maximize both farmer profit and pollinator resources, balancing nitrogen fixation, market price, and bloom timing.
3. Automated Monitoring – Drone‑based AI can count bee visits, detect disease outbreaks, and trigger precision pesticide applications only when pest thresholds are exceeded, minimizing unnecessary exposure.

9.4. Community‑Driven Governance

Self‑governing AI agents can be embedded within participatory platforms where beekeepers, farmers, and NGOs co‑design rules. For example, a blockchain‑backed system could allocate conservation credits to farms that maintain a certain percentage of natural habitat, with AI transparently verifying compliance through image analysis.

10. Looking Ahead: From Scenarios to Action

The three climate pathways illustrate a stark divergence:

Even the “best‑case” scenario (SSP1) cannot fully prevent bee declines because climate itself will alter flowering phenology and increase heat stress. However, land‑use decisions are the lever we can most directly influence. By integrating robust land‑use projections with pollinator‑focused conservation planning—and by leveraging AI agents to translate data into on‑the‑ground actions—we can narrow the gap between projected and desired outcomes.

Why It Matters

Land‑use change is the tangible expression of humanity’s response to climate change. The way we allocate fields, forests, and cities determines whether bees—our essential pollinators—will thrive or dwindle. The numbers are sobering: under a high‑emissions future, we could lose a sixth of global bee diversity, translating into billions of dollars of lost pollination services and cascading effects on food security. Yet the same models also show that proactive, sustainability‑oriented pathways can preserve and even expand pollinator habitats.

For the Apiary community, these projections are a call to action. They provide the scientific backbone for designing AI‑driven, landscape‑scale interventions that protect habitats, guide policy, and empower stakeholders. By aligning climate mitigation, land‑use planning, and pollinator conservation, we can safeguard the ecosystems that underpin both human livelihoods and the thriving AI agents that help us manage them. The future of our bees—and the future of resilient, biodiverse landscapes—depends on the choices we make today.