Share and intensity of work current AI systems can materially affect.
SOC 19-1023
Zoologists and Wildlife Biologists AI displacement risk
Camera-trap AI and acoustic classifiers process wildlife observations at scale, multiplying what one biologist can monitor. Field surveys, habitat assessment, and management recommendations for specific ecosystems keep the role field-anchored.
Likely potential for exposed tasks to move to software after workflow integration.
AI monitoring tools have made wildlife research dramatically more productive — more data, better coverage — without touching the fieldwork that generates ground truth or the ecological judgment that turns counts into management decisions.
Distribution
Where Zoologists and Wildlife Biologists sits across 620 tracked roles
Displacement pressure 24 — higher than 32% 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.
14 O*NET task statements matched to SOC 19-1023. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $76,780 (May 2025, US national). The latest BLS row matched SOC 19-1023.
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 Zoologists and Wildlife Biologists
SOC 19-1023 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 24/100 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 Zoologists and Wildlife Biologists
The current evidence import matched 14 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 tasks14
SOC19-1023
- Core task / ID 1493
Inventory or estimate plant and wildlife populations.
- Core task / ID 1496
Disseminate information by writing reports and scientific papers or journal articles, and by making presentations and giving talks for schools, clubs, interest groups and park interpretive programs.
- Core task / ID 23957
Develop, or make recommendations on, management systems and plans for wildlife populations and habitat, consulting with stakeholders and the public at large to explore options.
- Core task / ID 1492
Study animals in their natural habitats, assessing effects of environment and industry on animals, interpreting findings and recommending alternative operating conditions for industry.
- Core task / ID 1497
Study characteristics of animals, such as origin, interrelationships, classification, life histories, diseases, development, genetics, and distribution.
- Core task / ID 18615
Inform and respond to public regarding wildlife and conservation issues, such as plant identification, hunting ordinances, and nuisance wildlife.
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
Study animals in natural habitats
Exposure 24, automation 10%, augmentation 48%.
O*NET evidence: Study animals in their natural habitats, assessing effects of environment and industry ... (ID 1492)
Inventory and estimate wildlife populations
Exposure 42, automation 22%, augmentation 66%.
O*NET evidence: Inventory or estimate plant and wildlife populations. (ID 1493)
Write reports and scientific papers
Exposure 60, automation 32%, augmentation 72%.
O*NET evidence: Disseminate information by writing reports and scientific papers or journal articles, a... (ID 1496)
Advise on conservation and management
Exposure 32, automation 12%, augmentation 56%.
O*NET evidence: Inform and respond to public regarding wildlife and conservation issues, such as plant ... (ID 18615)
TaskExposureAutomationAugmentation
Study animals in natural habitats2410%48%
Inventory and estimate wildlife populations4222%66%
Write reports and scientific papers6032%72%
Advise on conservation and management3212%56%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowConservation Data Scientist
Training horizon: 4-9 months. Skill overlap 60. Wage preservation signal 106.
- Learn remote sensing analysis
- Validate AI species classifiers
- Build population models
adjacent role
LowWildlife Program Manager
Training horizon: 3-8 months. Skill overlap 64. Wage preservation signal 104.
- Manage habitat programs
- Coordinate agency stakeholders
- Write management plans
Comparison guides
Compare the next move before you commit
4-9 months
Zoologists and Wildlife Biologists to Conservation Data Scientist
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Zoologists and Wildlife Biologists into Conservation Data Scientist.
3-8 months
Zoologists and Wildlife Biologists to Wildlife Program Manager
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Zoologists and Wildlife Biologists into Wildlife Program Manager.
What the AI risk score means for Zoologists and Wildlife Biologists
The displacement pressure score for Zoologists and Wildlife Biologists is 24. 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. Write reports and scientific papers carries 32% automation pressure, while Write reports and scientific papers carries 72% 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: $76,780 (May 2025, US national). Employment context: Field biology role with AI-assisted monitoring. Typical education: Bachelor's degree; advanced degrees for research.
Wage vulnerability is 36, while transition feasibility is 62. 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
- Monitoring AI multiplies research capacity
- Fieldwork and ecology judgment persist
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Zoologists and Wildlife Biologists, 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.
Field research
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.
Population 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.
Conservation judgment
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.
Scientific writing
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 Conservation Data Scientist, such as learn remote sensing analysis.
- By 90 days, compare internal openings and external postings for Conservation Data Scientist or Wildlife Program Manager and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Zoologists and Wildlife Biologists
Will AI replace Zoologists and Wildlife Biologists?
Camera-trap AI and acoustic classifiers process wildlife observations at scale, multiplying what one biologist can monitor. Field surveys, habitat assessment, and management recommendations for specific ecosystems keep the role field-anchored. 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 Zoologists and Wildlife Biologists work are most exposed to AI?
Write reports and scientific papers and Inventory and estimate wildlife populations show the strongest automation pressure in this model. Write reports and scientific papers and Inventory and estimate wildlife populations are better treated as AI-augmented work.
What should Zoologists and Wildlife Biologists learn next?
Start with Field research, Population analysis, Conservation judgment. The most practical adjacent paths in this model are Conservation Data Scientist and Wildlife Program Manager.
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