Share and intensity of work current AI systems can materially affect.
SOC 19-1031
Conservation Scientists AI displacement risk
GIS analysis and remote sensing automate much of the mapping and monitoring that once filled field days. Advising landowners, designing conservation practices, and mediating land-use disputes keep scientists in the field and at the table.
Likely potential for exposed tasks to move to software after workflow integration.
Satellite monitoring covers more land than any field crew, which raises the value of the scientists who interpret it and implement practices on actual properties. Wildfire and water pressures are expanding the field's mandate.
Distribution
Where Conservation Scientists sits across 620 tracked roles
Displacement pressure 28 — higher than 43% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
30 O*NET task statements matched to SOC 19-1031. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $73,010 (May 2025, US national). The latest BLS row matched SOC 19-1031.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Conservation Scientists
SOC 19-1031 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 28/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Conservation Scientists
The current evidence import matched 30 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks30
SOC19-1031
- Core task / ID 22138
Apply principles of specialized fields of science, such as agronomy, soil science, forestry, or agriculture, to achieve conservation objectives.
- Core task / ID 22153
Plan soil management or conservation practices, such as crop rotation, reforestation, permanent vegetation, contour plowing, or terracing, to maintain soil or conserve water.
- Core task / ID 22135
Monitor projects during or after construction to ensure projects conform to design specifications.
- Core task / ID 22134
Advise land users, such as farmers or ranchers, on plans, problems, or alternative conservation solutions.
- Core task / ID 22133
Implement soil or water management techniques, such as nutrient management, erosion control, buffers, or filter strips, in accordance with conservation plans.
- Core task / ID 22140
Compute design specifications for implementation of conservation practices, using survey or field information, technical guides or engineering manuals.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Plan soil and water conservation practices
Exposure 42, automation 19%, augmentation 64%.
O*NET evidence: Plan soil management or conservation practices, such as crop rotation, reforestation, p... (ID 22153)
Advise landowners on conservation
Exposure 26, automation 8%, augmentation 50%.
O*NET evidence: Advise land users, such as farmers or ranchers, on plans, problems, or alternative cons... (ID 22134)
Analyze GIS and field data
Exposure 58, automation 32%, augmentation 70%.
O*NET evidence: Gather information from geographic information systems (GIS) databases or applications ... (ID 22139)
Monitor projects for compliance
Exposure 36, automation 16%, augmentation 56%.
O*NET evidence: Monitor projects during or after construction to ensure projects conform to design spec... (ID 22135)
TaskExposureAutomationAugmentation
Plan soil and water conservation practices4219%64%
Advise landowners on conservation268%50%
Analyze GIS and field data5832%70%
Monitor projects for compliance3616%56%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowNatural Resources Program Manager
Training horizon: 3-6 months. Skill overlap 70. Wage preservation signal 114.
- Manage conservation programs
- Own grant reporting
- Coordinate multi-agency projects
adjacent role
LowGIS Analyst
Training horizon: 3-6 months. Skill overlap 58. Wage preservation signal 102.
- Deepen spatial analysis skills
- Automate monitoring workflows
- Build land-use dashboards
Comparison guides
Compare the next move before you commit
3-6 months
Conservation Scientists to Natural Resources Program Manager
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Conservation Scientists into Natural Resources Program Manager.
3-6 months
Conservation Scientists to GIS Analyst
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Conservation Scientists into GIS Analyst.
What the AI risk score means for Conservation Scientists
The displacement pressure score for Conservation Scientists is 28. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Analyze GIS and field data carries 32% automation pressure, while Analyze GIS and field data carries 70% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $73,010 (May 2025, US national). Employment context: Land management role with climate and wildfire demand. Typical education: Bachelor's degree common.
Wage vulnerability is 38, while transition feasibility is 64. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Low displacement pressure
- Remote sensing extends coverage
- Implementation advice stays human
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Conservation Scientists, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Conservation planning
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
GIS analysis
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Landowner advising
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Field assessment
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Natural Resources Program Manager, such as manage conservation programs.
- By 90 days, compare internal openings and external postings for Natural Resources Program Manager or GIS Analyst and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Conservation Scientists
Will AI replace Conservation Scientists?
GIS analysis and remote sensing automate much of the mapping and monitoring that once filled field days. Advising landowners, designing conservation practices, and mediating land-use disputes keep scientists in the field and at the table. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Conservation Scientists work are most exposed to AI?
Analyze GIS and field data and Plan soil and water conservation practices show the strongest automation pressure in this model. Analyze GIS and field data and Plan soil and water conservation practices are better treated as AI-augmented work.
What should Conservation Scientists learn next?
Start with Conservation planning, GIS analysis, Landowner advising. The most practical adjacent paths in this model are Natural Resources Program Manager and GIS Analyst.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics