# Range Managers

> Research or study range land management practices to provide sustained production of forage, livestock, and wildlife.

- **SOC code:** 19-1031.02
- **Canonical URL:** https://singulariki.com/roles/role-19-1031-02
- **Also known as:** Natural Resource Specialist, Range Technician, Rangeland Management Specialist, Resource Manager, Conservationist, Land Management Supervisor, Natural Resource Manager, Range Management Specialist
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Regulate grazing, such as by issuing permits and checking for compliance with standards, and help ranchers plan and organize grazing systems to manage, improve, protect, and maximize the use of rangelands.
- Manage forage resources through fire, herbicide use, or revegetation to maintain a sustainable yield from the land.
- Coordinate with federal land managers and other agencies and organizations to manage and protect rangelands.
- Measure and assess vegetation resources for biological assessment companies, environmental impact statements, and rangeland monitoring programs.
- Maintain soil stability and vegetation for non-grazing uses, such as wildlife habitats and outdoor recreation.
- Study grazing patterns to determine number and kind of livestock that can be most profitably grazed and to determine the best grazing seasons.
- Offer advice to rangeland users on water management, forage production methods, and control of brush.
- Plan and direct construction and maintenance of range improvements, such as fencing, corrals, stock-watering reservoirs, and soil-erosion control structures.
- Mediate agreements among rangeland users and preservationists as to appropriate land use and management.
- Study rangeland management practices and research range problems to provide sustained production of forage, livestock, and wildlife.
- Tailor conservation plans to landowners' goals, such as livestock support, wildlife, or recreation.
- Develop technical standards and specifications used to manage, protect, and improve the natural resources of range lands and related grazing lands.

**Emerging tasks** (O*NET):
- Apply herbicide to eliminate harmful plants.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Active Listening _(essential_skill)_
- Written Comprehension _(ability)_
- Oral Expression _(ability)_
- Biology _(knowledge)_
- Reading Comprehension _(essential_skill)_
- Oral Comprehension _(ability)_
- Written Expression _(ability)_
- Problem Sensitivity _(ability)_
- English Language _(knowledge)_
- Speaking _(essential_skill)_
- Critical Thinking _(essential_skill)_
- Deductive Reasoning _(ability)_

**Skills in demand:**
- Active Listening _(Common Skill)_
- Biology _(Specialized Skill)_
- Reading Comprehension _(Common Skill)_
- English Language _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Geography _(Specialized Skill)_
- Complex Problem Solving _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- Writing _(Common Skill)_

**Tools & technology:**
- Adobe Photoshop _(hot technology)_
- ESRI ArcGIS software _(hot technology)_
- Facebook _(hot technology)_
- Linux _(hot technology)_
- Microsoft Access _(hot technology)_
- Microsoft Active Server Pages ASP _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Windows _(hot technology)_
- Microsoft Word _(hot technology)_
- Oracle Java _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 53rd percentile (Moderate) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 50th percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 59th percentile (Moderate) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 52nd percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 12th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 3.4% growth (About average); 2.5k annual openings; 28.5k → 29.5k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $67,950; 25,590 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-19-1031-02_