Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Occupation · SOC 19-4071.00
Provide technical assistance regarding the conservation of soil, water, forests, or related natural resources. May compile data pertaining to size, content, condition, and other characteristics of forest tracts under the direction of foresters, or train and lead forest workers in forest propagation and fire prevention and suppression. May assist conservation scientists in managing, improving, and protecting rangelands and wildlife habitats.
Also called: Forest Technician · Forestry Aide · Forestry Technician (Forestry Tech) · Resource Technician · Biological Science Aide · Timber Appraiser · Conservation Agent · Conservation Officer · Conservation Technician · Field Technician (Field Tech) · Forester Aide · Forestry Aid Technician
Job family: Life, Physical, and Social Science Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-19-4071-00/context.md directly.
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
25th-percentile task overlap — yet about 3,900 openings a year (-3.2% projected, BLS) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| LLM task exposure, γ (OpenAI / Eloundou) Moderate | 48th | 0.6 | |
| AI assistant applicability (Microsoft) Low | 6th | 0.0 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.3), and including AI-powered software (γ 0.6). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Develop and maintain computer databases. | 9.2% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -3.2% by 2034 |
| Projected annual openings | 3,900 |
| Employment 2024 → 2034 | 33,800 → 32,700 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
All 21 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
Newer responsibilities O*NET has flagged as growing for this occupation.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Public Safety and Security | 3.8 | |
| English Language | 3.7 | |
| Customer and Personal Service | 3.5 | |
| Law and Government | 3.4 | |
| Administration and Management | 3.4 | |
| Geography | 3.4 | |
| Mathematics | 3.3 | |
| Education and Training | 3.3 | |
| Biology | 3.3 | |
| Mechanical | 3.2 | |
| Computers and Electronics | 3.1 | |
| Personnel and Human Resources | 3.1 |
| Active Listening | 3.8 | |
| Critical Thinking | 3.8 | |
| Reading Comprehension | 3.4 | |
| Speaking | 3.3 | |
| Monitoring | 3.1 | |
| Writing | 3.0 |
| Oral Comprehension | 3.8 | |
| Oral Expression | 3.8 | |
| Problem Sensitivity | 3.8 | |
| Information Ordering | 3.8 | |
| Written Comprehension | 3.6 | |
| Deductive Reasoning | 3.6 | |
| Inductive Reasoning | 3.6 | |
| Near Vision | 3.5 | |
| Far Vision | 3.5 | |
| Visualization | 3.3 | |
| Written Expression | 3.1 | |
| Category Flexibility | 3.1 | |
| Selective Attention | 3.1 | |
| Arm-Hand Steadiness | 3.1 | |
| Manual Dexterity | 3.1 | |
| Speech Recognition | 3.1 | |
| Speech Clarity | 3.1 |
| Judgment and Decision Making | 3.3 | |
| Time Management | 3.3 | |
| Social Perceptiveness | 3.1 | |
| Coordination | 3.1 | |
| Instructing | 3.1 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Natural Resources and Conservation . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| High School Diploma | 35.3% | |
| Associate's Degree (or other 2-year degree) | 34.9% | |
| Bachelor's Degree | 14.0% | |
| Some College Courses | 10.4% | |
| Post-Secondary Certificate | 4.6% | |
| Post-Baccalaureate Certificate | 0.8% |
The interests and personal qualities O*NET associates with people who do this work.
| Nature/Outdoors | 6.6 | |
| Physical/Manual Labor | 5.0 | |
| Agriculture | 4.2 | |
| Life Science | 3.4 | |
| Protective Service | 3.3 | |
| Transportation/Machine Operation | 2.8 | |
| Mechanics/Electronics | 2.5 | |
| Management/Administration | 2.5 | |
| Teaching/Education | 2.4 | |
| Mathematics/Statistics | 2.1 |
| Realistic | 6.5 | |
| Conventional | 4.7 | |
| Investigative | 4.4 | |
| Social | 2.8 | |
| Enterprising | 2.5 |
| Dependability | 2.4 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $36,190 |
| 25th percentile | $42,560 |
| Median (50th) | $54,310 |
| 75th percentile | $66,020 |
| 90th percentile | $80,790 |
| People employed | 31,080 |
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Professional, Scientific, and Technical Services · Sector | 1,410 | $53,960 |
| Other Services (except Public Administration) · Sector | 680 | $45,040 |
| Educational Services · Sector | 380 | $49,120 |
| Utilities · Sector | 260 | $101,150 |
| Arts, Entertainment, and Recreation · Sector | 110 | $37,590 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 80 | $61,770 |
| Hydroelectric Power Generation · National industry | 50 | $84,200 |
| Agriculture, Forestry, Fishing and Hunting · Sector | — | $62,680 |
| Engineering Services · National industry | — | $46,860 |
| Research and Development in the Social Sciences and Humanities · National industry | — | $39,520 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Utilities · Sector | 2.23× | 260 |
| Other Services (except Public Administration) · Sector | 0.76× | 680 |
| Professional, Scientific, and Technical Services · Sector | 0.65× | 1,410 |
| Arts, Entertainment, and Recreation · Sector | 0.21× | 110 |
| Educational Services · Sector | 0.14× | 380 |
Part of the Agriculture and Energy & Natural Resources career clusters.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Forest and Conservation Technicians — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
See where this work sits in the bigger picture.
Forest and Conservation Technicians show 25th-percentile AI task overlap — and about 3,900 annual U.S. openings
Forest and Conservation Technicians show 25th-percentile AI task overlap — and about 3,900 annual U.S. openings • Forest and Conservation Technicians rank in the 25th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 3,900 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be declining (-3.2%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $54,310, across about 31,080 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Forest and Conservation Technicians". https://singulariki.com/roles/role-19-4071-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Forest and Conservation Technicians." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026. https://singulariki.com/roles/role-19-4071-00
Singulariki. (2026). Forest and Conservation Technicians. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-19-4071-00
@misc{singulariki-role-19-4071-00,
title = {Forest and Conservation Technicians},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-19-4071-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.