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Conservation scientists

Also called Soil conservationist, Natural resource conservationist, Range conservationist, Conservation biologist, Natural resource scientist

In brief

In May 2025, BLS counted 25,950 US conservation scientists, with median pay of $73,010 a year. Of the BLS counts since 2003, the May 2025 count is the highest. The earliest figure here is an estimate of 8,000 from 1940, not measured the same way as today's count. The last full technology era for conservation scientists: AI camera-trap processing and bioacoustic monitoring, 2018 to 2023.

What the sources say

Official outlook

BLS projects employment to grow 5.3% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Conservation scientists: jobs and pay since 1940 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/19-1031

Chicago

Future History. "Conservation scientists: jobs and pay since 1940." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/19-1031.

BibTeX

@misc{futurehistory_19_1031, author = {{Future History}}, title = {Conservation scientists: jobs and pay since 1940}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/19-1031}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
195019752000now
2026
Known then as Soil Erosion Service Conservationist (New Deal emergency program)
Known then as Soil Conservationist / Range Conservationist (Soil Conservation Service)
Known then as Soil Conservationist / Natural Resource Conservationist (NRCS era)
Known then as Conservation Scientists (BLS SOC 19-1031)
Known today as Conservation Scientists (BLS SOC 19-1031)
Projected to still be called Conservation Scientists (BLS SOC 19-1031)
Data from the May 2025 survey
US Employment
26K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$73,010

≈ $71,138 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1928

Hugh Hammond Bennett and William Ridgely Chapline co-author "Soil Erosion: A National Menace" for the USDA Bureau of Soils. The bulletin is the founding document of American soil conservation science: a systematic national survey of erosion damage, its economic costs, and what could be done about it. Bennett had been documenting erosion since joining the USDA in 1903; this was his first major public advocacy piece and it launched the political campaign that culminated in the SCS seven years later.

Beat · 1935

April 27, 1935: President Roosevelt signs the Soil Conservation and Domestic Allotment Act, creating the Soil Conservation Service as a permanent USDA agency and establishing "soil conservationist" as a recognized federal profession. Hugh Hammond Bennett is named chief. Within months the SCS has hired hundreds of professional conservationists and deployed them across the country to work directly with farmers. The profession is born with federal statutory backing, a nationwide field infrastructure, and a legislated mission.

Beat · 1972

Landsat 1 launches on July 23, 1972, providing the first continuous multispectral satellite record of Earth's land surface and fundamentally changing what remote monitoring of conservation project areas was possible. USGS and NASA make Landsat data freely available; by the 1980s conservation scientists are using Landsat imagery to map vegetation change, deforestation, and land degradation at scales no field program could match. The 50-year Landsat archive that accumulates from 1972 onward becomes the most important single data resource in the history of conservation science.

Beat · 1985

The Food Security Act of 1985 (the 1985 Farm Bill) creates three programs that permanently reshape the conservation scientist's workload: the Conservation Reserve Program (CRP), which pays farmers to retire erodible land into 10-15 year conservation cover; "sodbuster," which ties crop program benefits to not plowing highly erodible land; and "swampbuster," which ties benefits to not draining wetlands. These programs require USDA NRCS field scientists to conduct eligibility determinations, design conservation systems, and monitor compliance on tens of millions of enrolled acres. The Farm Bill becomes the primary engine of conservation scientist employment from 1985 onward.

Beat · 1994

October 20, 1994: Congress renames the Soil Conservation Service to the Natural Resources Conservation Service as part of the Federal Crop Insurance Reform and Department of Agriculture Reorganization Act. The rename signals a broadened mission: no longer just soil erosion control but integrated natural resource management, including water quality, wetlands, wildlife habitat, and ecosystem services. The professional title shifts accordingly, with "conservation scientist" gradually replacing the older "soil conservationist" framing in job postings and BLS classification.

Beat · 2008

The 2008 Farm Bill establishes the Conservation Stewardship Program (CSP), one of the largest conservation programs in USDA history, providing annual payments to farmers and ranchers who maintain existing conservation systems and adopt new ones on working agricultural land. Unlike CRP (which removes land from production), CSP works on actively farmed land. The program drives a new generation of NRCS conservation planning work: assessing stewardship performance on farms, designing conservation enhancements, and monitoring long-term outcomes across millions of enrolled acres.

Beat · 2026

March 2026: Planet Labs announces a partnership with Anthropic to use Claude LLM to analyze Planet's geospatial satellite imagery at scale for conservation applications, enabling near-real-time land change detection across the Earth's entire land surface. In the same month, EarthRanger and SMART patrol management announce merger into the unified SERCA platform, consolidating real-time wildlife monitoring and anti-poaching patrol management into a single system deployed in 180+ protected areas. These announcements mark the moment when AI-integrated conservation science at planetary scale transitions from experimental to operational.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace conservation scientists? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+5.3%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 19-1031 (Conservation scientists): 27.7 thousand jobs in 2025, projected 29.2 thousand in 2035, a change of +1.5 thousand (+5.3%). Matrix employment counts all jobs in the occupation, including self-employed workers (0.4% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
55.1%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 19-1031.00 Conservation Scientists. Shown: human-rated β = 0.551, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.271 and γ (all LLM-assisted) 0.831; GPT-4-rated β 0.432. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for conservation scientists

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereProcess and triage camera-trap image datasets using MegaDetector and Google SpeciesNet: upload field-collected image batches (typically 50,000–500,000 images per monitoring season) to a MegaDetector pipeline that automatically removes blank frames caused by wind, vegetation movement, or sensor false-triggers

Process and triage camera-trap image datasets using MegaDetector and Google SpeciesNet: upload field-collected image batches (typically 50,000–500,000 images per monitoring season) to a MegaDetector pipeline that automatically removes blank frames caused by wind, vegetation movement, or sensor false-triggers; pass animal-positive frames to SpeciesNet or a site-specific classifier that identifies species across ~2,500 categories; review AI-flagged detections with low confidence scores manually; compile species occurrence records with location, date/time, and detection confidence for entry into biodiversity databases and project monitoring reports. This three-stage workflow (MegaDetector → species classifier → manual verification) compressed a multi-year image backlog to weeks at multiple conservation organizations by 2026.[4],[3],[5]

Where your edge is

SpeciesNet and MegaDetector now eliminate the blank-frame bottleneck and handle routine species ID accurately for common species: adopt these pipelines for every multi-season camera-trap deployment. Your irreplaceable contribution is the ecological interpretation layer: reviewing low-confidence detections (especially rare species, juveniles, or partial-frame images), assessing whether a detection pattern reflects genuine habitat use or an artifact of camera placement, and integrating species occurrence data with habitat covariates to draw population-level inferences. For legally significant detections (listed species, invasive species triggering a management response), document your manual verification with the original image: AI-automated records alone will not withstand regulatory scrutiny.

AI is sitting alongside you hereQuantify forest carbon stocks and monitor conservation project additionality for voluntary carbon market MRV (measurement, reporting, and verification) using AI-enabled remote sensing platforms: use Pachama's satellite + ML platform to estimate above-ground biomass and carbon density across project forests

Quantify forest carbon stocks and monitor conservation project additionality for voluntary carbon market MRV (measurement, reporting, and verification) using AI-enabled remote sensing platforms: use Pachama's satellite + ML platform to estimate above-ground biomass and carbon density across project forests; compare project area carbon trajectories against dynamically generated control areas to assess additionality; monitor for leakage (deforestation displaced outside the project boundary); review AI-generated carbon reports against published allometric equations and carbon accounting standards (VCS/VM0015, Plan Vivo); compile MRV reports for carbon credit issuance by registries (Verra, Gold Standard). This specialization track emerged directly from the AI remote sensing revolution and commands substantially higher compensation than traditional field-science roles.[12],[15],[16]

Tools picking this up
Where your edge is

Pachama and similar AI carbon platforms make remote-sensing-based forest carbon quantification tractable for project developers and conservation scientists without custom satellite analysis pipelines: the barriers to entering the voluntary carbon market as a project developer or MRV provider have dropped dramatically. However, carbon registry standards still require field plot validation of remote-sensing biomass estimates: AI platform outputs must be reconciled against allometric inventory data from field-measured plots. Build competency in carbon accounting methodologies (VCS VM0015, VM0007, jurisdictional REDD+ frameworks) alongside the remote sensing tooling: the combination is what differentiates a forest carbon MRV specialist from a general remote sensing analyst, and the former commands $80,000–$140,000+ in the private sector versus $55,000–$80,000 for NRCS field scientist roles.

AI is sitting alongside you hereMonitor land cover change, habitat loss, and conservation project compliance at landscape scale using Google Earth Engine (GEE): load time-series Landsat and Sentinel-2 imagery for the project area

Monitor land cover change, habitat loss, and conservation project compliance at landscape scale using Google Earth Engine (GEE): load time-series Landsat and Sentinel-2 imagery for the project area; run ML classification algorithms (Random Forest, Support Vector Machine, U-Net) within GEE to generate annual land cover maps; compare time-series stacks to detect deforestation, wetland drainage, or vegetation conversion; generate statistical summaries of habitat change by cover class and land tenure; export GIS-ready layers and change-detection reports for inclusion in conservation management plans, grant reports, and regulatory compliance submissions. Supplement with Planet Labs daily imagery for near-real-time alerts on active disturbance events in high-priority areas.[8],[17],[9]

Where your edge is

Google Earth Engine ML classification is highly efficient for broad land cover categories (forest/non-forest, wetland/upland, cropland) but degrades in accuracy for fine-grained habitat distinctions (wet meadow vs. mesic grassland, shrub-scrub successional stages) that matter most for species-specific conservation assessments. Always ground-truth a stratified random sample of AI-classified pixels against field observations or high-resolution imagery before using land cover maps to inform regulatory findings or grant accountability reporting. Planet Labs near-real-time alerts are powerful for detecting new disturbances but require expert interpretation to distinguish conservation-significant land clearing from permitted agricultural activity: context that the satellite alone cannot provide.

Where conservation scientists go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Sustainability Specialists

Conservation scientists with remote sensing, land cover analysis, and ecological baseline assessment skills are well-positioned for Sustainability Specialist roles focused on nature-based solutions (NbS), corporate biodiversity commitments, and forest carbon markets. The EU CSRD and SEC climate disclosure rules are driving demand for corporate sustainability professionals with quantitative natural capital and biodiversity skills: exactly the competencies conservation scientists have. Pachama, Verra-registered project developers, carbon asset managers, and large corporations with net-zero land commitments are actively hiring conservation scientists into Sustainability Specialist roles with substantially higher compensation ($80,000–$140,000+ vs. $55,000–$80,000 for NRCS). The forest carbon MRV specialization path (Task T08) is the most direct bridge: a conservation scientist with Pachama or NCX platform proficiency and carbon accounting methodology knowledge can transition into the private sustainability sector with two to three months of targeted skill-building.

What you'd add
  • · Carbon accounting frameworks: VCS (Verra) VM0015/VM0007 REDD+ methodologies, Gold Standard VER protocols, Taskforce on Nature-related Financial Disclosures (TNFD)
  • · Corporate net-zero and biodiversity target frameworks: SBTi land sector guidance, SBTN (Science Based Targets for Nature) target-setting process, TCFD physical risk disclosure
  • · Forest carbon MRV tools: Pachama, NCX, Sylvera platform proficiency; allometric equation selection; remote-sensing biomass validation field protocols
  • · ESG data management: reporting to CDP, GRI (GRI 304 Biodiversity), SASB sector-specific standards; sustainability management software (Watershed, Salesforce Sustainability Cloud)
  • · Carbon market mechanics: voluntary carbon market structure, registry project registration, buyer due diligence processes, carbon credit quality assessment
What it takesSome new skills to pick up
Tools of the era

How has the work of conservation scientists changed since 1933?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Field notebook, Munsell color chart, and contour plow (Dust Bowl-era manual practice)

    The first generation of SCS conservationists worked entirely with hand tools and trained judgment. The Munsell soil color chart (developed by artist Albert Munsell in 1905, adopted by pedologists in the 1930s) gave soil scientists a standardized vocabulary for describing soil horizons in the field. Conservation plans were drafted by hand on paper maps; erosion-control structures were designed using tables in USDA technical handbooks. The entire practice was built around the field visit: reading the land, talking to the farmer, sketching a plan on paper.

  • Aerial photography and stereoscopic mapping (photogrammetry for soil and land surveys)

    The USDA launched systematic aerial photography programs covering agricultural land in the 1950s and 1960s, and by the mid-1960s stereoscopic photointerpretation had become a standard tool for soil survey mapping. Conservation scientists used aerial photo pairs with stereoscopes to delineate soil boundaries, identify erosion features, map rangeland condition, and detect wetland hydrology signatures across larger areas than field traverses alone could cover. The National Cooperative Soil Survey accelerated coverage as aerial photography became cheaper and more systematic. This era also saw the first use of satellite imagery for land monitoring: Landsat 1, launched in 1972, provided the first continuous multi-spectral satellite record of the Earth's land surface and introduced conservation scientists to remote sensing as a monitoring tool.

    Effect on the work

    Aerial photointerpretation expanded the area a single conservationist could survey and map meaningfully, roughly doubling effective coverage per field day for mapping-intensive work, without reducing headcount because demand for conservation surveys expanded at least as fast.

  • GIS software and GPS field data collection (ArcInfo, early ArcGIS, Trimble GPS)

    The introduction of desktop GIS (ESRI ArcInfo, commercially available from 1982; ArcView from 1991) and handheld GPS receivers (civilian GPS accuracy improved after Selective Availability was disabled in May 2000) transformed conservation science practice in the 1990s. What had previously required days of field traverses with plane tables and compasses could now be captured with GPS points in the field and processed into digital maps overnight. Conservation scientists began producing spatial data layers for soil type, vegetation cover, watershed boundaries, and wetland delineation that could be overlaid, queried, and shared. The Farm Bill program expansions of the 1985 and 1990 acts drove demand for GIS-based conservation planning at the same time the tools became accessible.

    Effect on the work

    GIS adoption increased individual productivity for mapping and spatial analysis tasks substantially. The primary effect was not workforce reduction but capability expansion: conservation scientists who previously could not produce spatially explicit conservation plans could now do so routinely, raising the analytical quality of field work without proportional increases in staff.

  • Google Earth Engine and multi-sensor satellite monitoring (cloud GIS, Landsat time series, MODIS)

    Google Earth Engine, announced in 2010 and made widely available to the research community around 2012-2013, gave conservation scientists access to petabytes of satellite imagery (Landsat archive back to 1972, MODIS back to 2000, Sentinel-1/2 from 2014 onward) with built-in cloud computing, eliminating the need for local storage and processing infrastructure. A single conservation scientist could now analyze decades of land cover change across thousands of hectares, running time-series analyses that previously required a university research team. GEE became the dominant platform for deforestation monitoring, habitat change detection, and conservation project compliance verification.

    Effect on the work

    GEE and cloud GIS dramatically reduced the time required for landscape-scale remote sensing analysis. Tasks that previously required weeks of data download, processing, and analysis could be completed in hours. The primary labor-market effect was not displacement but skill-requirement elevation: conservation scientist job postings increasingly listed GEE, Python, and R as requirements by 2015-2018, raising the technical floor of entry-level hiring.

  • AI camera-trap processing and bioacoustic monitoring (MegaDetector, BirdNET, Wildlife Insights)

    Microsoft AI for Earth released MegaDetector as an open-source model in 2019 for detecting animals in camera-trap images, solving a problem that had been a multi-year bottleneck at conservation organizations: the backlog of unreviewed camera-trap images. A single three-month wildlife monitoring deployment could generate millions of images; reviewing them manually had previously required months of volunteer or staff time. MegaDetector reduced blank-frame removal from days to minutes and enabled three-stage automated workflows (detection, species classification, manual verification of low-confidence results). The Cornell Lab of Ornithology released BirdNET in 2019, enabling automated detection of 6,000+ bird species from passive acoustic recordings. By 2022-2023, these AI tools had become standard practice at wildlife agencies and conservation NGOs worldwide.

    Effect on the work

    Anthropocene Magazine (May 2026) documented that AI camera-trap workflows compressed multi-year image backlogs to weeks at multiple conservation organizations: "if we can process data faster, we can respond faster, and that's really what matters for conservation." The labor effect was primarily augmentative: the same field staff could now process monitoring data from ten times more camera deployments annually, enabling landscape-scale wildlife surveys that were previously cost-prohibitive.

  • Generative AI, planetary-scale satellite AI, and forest carbon MRV platforms (SpeciesNet, EarthRanger, Pachama, Planet+Claude)

    The 2023-2026 period marked an acceleration in purpose-built AI for conservation science across every major task category. Google released SpeciesNet in 2025, trained on 65 million labelled camera-trap images and classifying ~2,500 animal categories. Allen AI's EarthRanger unified real-time GPS telemetry, camera-trap alerts, drone feeds, and patrol data for more than a hundred protected areas across many countries. Planet Labs announced a partnership with Anthropic in March 2026 to use Claude LLM for near-real-time analysis of planetary satellite imagery for conservation applications. Forest carbon MRV platforms (Pachama, NCX, Sylvera) created an entirely new specialization track within conservation science: measuring, reporting, and verifying forest carbon stocks for voluntary carbon markets using AI remote sensing, with compensation substantially higher than traditional NRCS field roles. LLMs (ChatGPT, Claude) accelerated conservation plan drafting and environmental assessment writing. The conservation scientist who builds fluency with this tool stack can monitor more land, respond faster, and access markets that did not exist five years ago.

    Effect on the work

    The AI tool revolution has not reduced headcount projections; it has elevated the skill threshold and created new specialization tracks (forest carbon MRV, AI wildlife monitoring) with higher compensation ceilings.

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Conservation Scientists employment and pay by year

On record since1933
Latest US employment25,950 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$73,010 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+5.3% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 27 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
19408,000n/aEst., Encyclopedia.com Researched contextReport
197012,000n/aEst., NRCS Researched contextReport
199015,000n/aEst., National Agricultural Law Center Researched contextReport
200012,980n/aBLS OEWS 2000 Official statisticReport
200313,780$51,230BLS OEWS May 2003 Official statisticReport
200414,290$52,480BLS OEWS May 2004 Official statisticReport
200515,540$53,350BLS OEWS May 2005 Official statisticReport
200616,000$54,970BLS OEWS May 2006 Official statisticReport
200716,570$56,150BLS OEWS May 2007 Official statisticReport
200815,830$58,720BLS OEWS May 2008 Official statisticReport
200916,810$60,160BLS OEWS May 2009 Official statisticReport
201018,880$59,310BLS OEWS May 2010 Official statisticReport
201118,910$59,530BLS OEWS May 2011 Official statisticReport
201218,460$61,100BLS OEWS May 2012 Official statisticReport
201318,590$61,220BLS OEWS May 2013 Official statisticReport
201419,210$61,860BLS OEWS May 2014 Official statisticReport
201520,200$61,110BLS OEWS May 2015 Official statisticReport
201620,470$61,810BLS OEWS May 2016 Official statisticReport
201722,040$61,480BLS OEWS May 2017 Official statisticReport
201822,200$61,310BLS OEWS May 2018 Official statisticReport
201922,070$62,660BLS OEWS May 2019 Official statisticReport
202022,020$64,020BLS OEWS May 2020 Official statisticReport
202122,550$63,750BLS OEWS May 2021 Official statisticReport
202222,880$64,460BLS OEWS May 2022 Official statisticReport
202322,790$68,750BLS OEWS May 2023 Official statisticReport
202425,590$67,950BLS OEWS May 2024 Official statisticReport
202525,950$73,010BLS OEWS May 2025 Official statisticReport
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