PayCrunch Research · The exact AI playbook for your profession, sourced to the U.S. Bureau of Labor Statistics

PayCrunch AI Playbook · Science

What lifts a wildlife biologist to the top of the range

$177,760top of the range in California · middle $76,780 / yr
AI augments this role

Wildlife Biologists in the United States earn a median of $76,780 a year. Pay starts near $49,100. Pay reaches $177,760 at the top of the range in California, the best-paying state for this work among those with at least 500 people in the job.

Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Zoologists and Wildlife Biologists, SOC 19-1023). Last checked 9 September 2026.

Entry level
$49,100
Top of the range · California
$177,760
Education
Master's degree in Wildlife Biology
Lower disruption Higher exposure AI augments this role
Entry · $49,100 Top of range · $177,760 (California) Middle $76,780

Wages — U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Zoologists and Wildlife Biologists). Top of the range is the highest state-level figure among states with at least 500 people in the job. AI-impact rating is PayCrunch's editorial assessment. Updated September 2026.

🆕 New & Trending AI Tools for Wildlife BiologistReviewed September 2026

We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Wildlife Biologist work right now.

Julius AINEWFree / $20 mo

AI data analyst that runs statistics and charts from plain-language prompts.

How a Wildlife Biologist uses it: analyze datasets and generate figures without writing code

NotebookLMNEWFree / $7.99 mo

Google tool that answers questions grounded only in the documents you give it — with citations.

How a Wildlife Biologist uses it: load your own manuals, policies, or PDFs and ask questions that stay accurate to the source

ElicitFree / $12 mo

AI research assistant that finds and summarizes papers.

How a Wildlife Biologist uses it: run a literature review and extract findings across dozens of papers fast

ConsensusFree / $9 mo

AI search that answers questions from peer-reviewed research.

How a Wildlife Biologist uses it: get evidence-backed answers with the studies behind them

SciSpaceFree / paid

AI that explains papers and helps with literature review.

How a Wildlife Biologist uses it: decode dense papers and trace citations quickly

SciteFree / $20 mo

Shows whether other studies support or contradict a paper's claims (Smart Citations).

How a Wildlife Biologist uses it: check if a finding is actually backed by the wider literature before you cite it

ChatGPTFree / $20 mo

The most-used AI assistant — writing, analysis, research, and images from a plain-language chat.

How a Wildlife Biologist uses it: draft emails and documents, summarize long files, and get instant answers to on-the-job questions

ClaudeFree / $20 mo

AI assistant known for careful writing, long-document analysis, and coding.

How a Wildlife Biologist uses it: analyze big reports or spreadsheets and turn messy notes into clean, finished writing

Google GeminiFree / $20 mo

Google's AI assistant, built into Gmail, Docs, and Search.

How a Wildlife Biologist uses it: draft and reply inside Google Workspace and research without leaving the page

A field day with a notebook

A wildlife biologist spends a surprising amount of time where the animals are, and another surprising amount of time explaining what was found. One day is a survey along a transect, a check of nests or tracks, or a count that has to be done the same way as last season so the comparison means something. Another day is a desk: entering observations, writing a report a manager can use, or answering a question about a project that will cross habitat. The job is field biology, not a zoo clinic. You are not running a hospital for captive animals. You are studying wild populations, their habitat, and the decisions people make that help or harm them.

The science is practical. You design or follow a method someone already approved. You keep notes a stranger could audit. You learn to tell similar species apart, or you send the hard cases to someone who can. You use maps and location data without treating the software as the whole skill. Weather, access, and the breeding calendar run the schedule more than a corporate quarter does. A missed week in the field can erase a season of inference. That is why agencies and consulting firms care about people who finish the work and label it honestly, including the days the animals did not appear.

Communication is half the occupation once you leave the technician years. Landowners, hunters, tribal staff, county planners, and engineers all have a stake in the same acre. You translate findings without inflating them. You say what the data can support and what it cannot. A biologist who only talks to other biologists will struggle in a permitting meeting. A biologist who bends a result to please a client will struggle worse, later, when the record is read. The craft is accuracy plus enough plain language that a decision-maker does not have to guess.

Graduate study, and a licence that often is the wrong word

A graduate degree is the usual door to a wildlife biologist title. A bachelor's degree in wildlife biology, zoology, ecology, or a related field can get you field technician work. The biologist posting, especially with a state agency or a federal office, often asks for the graduate degree or for a combination of study and experience the announcement spells out. Read the announcement rather than a forum summary. Coursework in ecology, statistics, and the taxon you claim matters because the hiring form will ask. A thesis or a major project matters because it proves you can finish a question and write it down.

A state or federal wildlife job may require a degree rather than a licence. That distinction saves people a lot of confusion. You are not, in the ordinary case, collecting a professional licence the way a veterinarian or an engineer does. The agency hires you into a classification that already embeds the education requirement. Some tasks still sit under permits: handling, banding, or work on protected species may require authorization from the agency that governs that activity. Those permits are permissions for the project, often held by the employer or the principal investigator, and they are not a general licence to call yourself a biologist. If a private firm tells you a personal licence is mandatory, ask which board issues it. Many times the honest answer is that the degree and the experience are the requirement, plus whatever permit the project needs.

Degree, permit, and title

The degree shows you were trained. A project permit shows a specific activity was authorized. The job title shows an employer hired you. Keep those three apart when you read a posting or introduce yourself.

Who actually posts the jobs

State wildlife agencies, federal land and wildlife offices, universities, nonprofits, and environmental consulting firms hire this work. Agencies tend to hire through formal announcements with veterans' preference rules, graded questionnaires, and a slow calendar. Consulting firms hire faster and tie the work to client projects: surveys before a road, a pipeline, a wind site, or a development. Universities hire technicians on soft money and hire biologists into research staff or faculty paths that are harder to enter. Nonprofits may mix science with public programs. The same resume will not sound right in all four places. An agency wants the announcement's words matched with real experience. A firm wants to know you can finish a survey and a report on a deadline.

Applications fail in ordinary ways. People list passion and omit methods they have actually used. People claim a taxon they met once. People ignore the instruction to include transcripts. Fix those before you apply widely. A season of technician work with a named protocol, a supervisor who will answer the phone, and a report with your name on it beats a vague summer of helping. If you are still in graduate school, publish or at least finish a thesis chapter you can describe. Hiring panels can tell a finished project from an intention.

Geography is part of the application. Field jobs cluster where the habitat and the public land are, and that map rarely matches the places where salaries look highest on a chart. A posting in a remote station may include housing or it may assume you already own a truck and a tolerance for long drives. Ask. Urban and suburban consulting jobs exist too, often around permitting. Neither path is more real. They produce different weeks, and the pay comparison later in this piece should be read beside the week you are actually being offered.

Technician seasons, then a project with your name

The early career is seasonal for a lot of people. You take a summer survey, a winter count, a graduate assistantship. The pay can look like the entry figure because the appointment is short and closely supervised. The point of those seasons is skill and a supervisor's trust. String them into a story: the same taxon, better methods, more responsibility for the data. A scatter of unrelated gigs is harder to explain than a line of work that got sharper.

Between seasons, the work that gets you hired is often unglamorous writing. Clean a dataset. Label photographs so someone else can check the identification. Draft the methods section while you still remember why a site was dropped. Volunteer coordination and landowner visits count when you can describe what you were responsible for, and they count less when you only stood nearby. Read reports from the agency or firm you want to join. Their structure tells you how they think. Matching that structure in your own writing sample, with your own real results, is more persuasive than a generic cover letter about loving the outdoors.

The biologist title arrives when you design part of the work, supervise technicians, or sign a report. In an agency, that may be a permanent classification after temporary seasons. In a firm, it may be the moment clients hear your name. You still go to the field, and you also spend more time in meetings, budgets, and review. Some people miss the field and step back toward crew lead roles on purpose. Some move into program management, policy, or a teaching job. A few become the specialist everyone calls for one species or one region. That specialist path is slow and legible. It is built from correct identifications and reports that held up, not from a personal brand. Along the way, learn how your employer handles public records and controversial projects. A survey that will affect a permit can draw attention you did not seek. Write so the file can be read by someone who disagrees with the project and still finds the methods clear. That discipline protects the science and protects you. It also makes you more useful to the next hiring panel, because they can see you already understand that field biology becomes a public document.

Private and public employers promote differently. A firm may raise you when you can sell and deliver projects. An agency may move you on a grade schedule that cares about time in grade and a vacancy. Neither system is a verdict on your science. Learn the one you are in. If you want the other, translate your experience into its language before you apply, and expect the degree requirement to remain. Field biology does not become a zoo clinic just because the employer changes. The animals in question are still wild, and the product is still knowledge a decision can use.

California holds both figures, in this state order

Occupational Employment and Wage Statistics for May 2025 report this work in the zoologists and wildlife biologists series. Early-career pay often clusters near $49,100, and national median pay sits at $76,780. The step between them is $27,680. California holds both the high end of the published range, $177,760, and the highest median, $98,530. Those are different statistics. The high end is the top of the published range. The median is the middle of pay in the state. National median pay sits $21,750 under California's median. The distance from the national median to California's high end is $100,980, a span you should not describe as a typical salary.

Five state medians, in this order, are California at $98,530, Alaska at $90,370, Oregon at $85,150, Washington at $83,780, and Colorado at $83,300. A zoologist write-up reverses that order, starting from Colorado and ending in California, so the two lists are the same places read from opposite ends. All five medians sit above the national median. Alaska's $90,370 is the second median, not the high end. Oregon, Washington, and Colorado sit closer together, from $85,150 down to $83,300. Texas shows the lowest median in the release, at $49,100, the same dollar figure as the national entry level and a different fact. The gap from that Texas median to California's median is $49,430. When you cite California, say which statistic you mean. $98,530 is the median. $177,760 is the high end of the published range.

A public offer and a consulting offer

Put the offer next to the duty. Pay near $49,100 can match a technician season or a first supervised role, and it matches the Texas median as well, so a Texas offer at that level is sitting on the lowest published median and on the national entry figure at once. It is a weak match for someone who already holds a graduate degree, leads a crew, and signs reports. The $27,680 step toward $76,780 is the published distance from entry to the national middle. In an agency, ask which grade the duties belong to. In a firm, ask whether you are billing as a technician or as a biologist. The title on the email is less useful than that answer.

Then use the state order without mixing the California pair. An offer at the national median sits under California, Alaska, Oregon, Washington, and Colorado. California's median is $98,530, which is $21,750 above the national median, and California's high end of $177,760 is a different statistic entirely. Alaska's median is $90,370. Oregon's is $85,150. Washington's is $83,780. Colorado's is $83,300. A remote duty station and a city consulting job can both be honest offers at very different lives. Compare housing, field season length, and whether a permit and a degree are already in hand. You are being hired for field biology. The wage conversation should stay as specific as the data you would be willing to sign.

The top of Wildlife Biologist pay — and how to get there with AI

$177,760what Wildlife Biologist pay reaches in California

Highest state-level top-of-range annual wage for Zoologists and Wildlife Biologists, among states with at least 500 people in the job. U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025.

And the role it leads to — Biological Scientists, All Other — reaches $211,910 in District of Columbia.

$49,100entry$76,780middle$177,760top end

Two wildlife biologists can report the same population number; the one paid near the top of this range attaches a stated method and a file anyone can rerun, so the number still holds when a permit or a court date depends on it.

Inventorying and estimating plant and wildlife populations, then turning those numbers into habitat management plans, is most of this job. The estimates usually live in a personal spreadsheet and the method lives in somebody's memory, so each season starts by rebuilding both. Biologists who close that gap keep counts in Microsoft Access with the GPS software output attached, script the cleaning and the density estimate in Python, and let a model draft a first pass of the literature review before every source is checked by hand. That turns fieldwork into something an agency can defend when it checks compliance with environmental laws.

Your playbook, by where you are now

Just startingCapture the field data once, properly

  1. Fix the datasheet, the species codes and the file names before the first survey day, and hold the crew to them.
  2. Pull GPS software tracks and waypoints into Microsoft Access the evening you collect them, while an obvious error is still fixable.
  3. Rewrite one page of field notes each week into the plain wording a public question about nuisance wildlife would need.
  4. Learn enough Python to clean a season of counts without touching cells by hand.

What proves it: A season of population counts a stranger could reload and recompute without calling you.

Realistic span: the first two field seasons

A few years inTurn your method into the office's method

  1. Set out the estimation method with its assumptions and its known weak spots, then have two other people run it and compare results.
  2. Build ESRI ArcGIS software layers that show effort as well as animals, since a blank map square usually means nobody looked there.
  3. Point NotebookLM at the agency's back catalogue of reports so a literature review starts from what colleagues already found.
  4. Take the drafting of one habitat management plan and run the stakeholder consultation yourself.
  5. Present a finding to a school group or a hunting club, then fix whatever needed a second explanation.

What proves it: A survey method the office adopts, with your name on the version history.

Realistic span: years three through six

ExperiencedOwn what the numbers get used for

  1. Set the monitoring programme for a region rather than a site, scheduling it in Microsoft Project so budgets and field windows line up.
  2. Sit in the compliance conversation, checking work against environmental law and deciding when law enforcement gets notified.
  3. Publish the method itself alongside the findings, so other agencies inherit it instead of inventing their own.
  4. Move toward the wider biological science posts, and note that California pays wildlife biologists more than anywhere else.

What proves it: A regional management plan built on data you standardised, run by staff without you.

Realistic span: seven years and beyond

The next 90 days

Pick the population inventory you ran most recently and rebuild it as something reproducible. Put the raw counts and GPS records in one Microsoft Access table, write the estimate as a Python script instead of a chain of spreadsheet formulas, and write two pages covering how sites were chosen, what was counted, what was missed and how confident you are. Then have a colleague run the script on the same data and see whether the number matches. Most wildlife biologists have never done this for even one survey, which is why so much fieldwork gets recollected rather than reused. Those two pages are also the fastest way to show a hiring panel that your counts can be defended.

Wage figures: BLS OEWS, May 2025. The playbook is PayCrunch editorial guidance, not a guarantee of pay or placement.

Careers related to Wildlife Biologist

Similar pay, same field

Where this can lead

Every figure is the national median from the U.S. Bureau of Labor Statistics (OEWS) shown on that role’s own page.

Never used AI before? Start here (2 minutes).

Point AI at your biggest data bottleneck first. If you fly drone or aerial surveys, train a detector in Picterra or Roboflow to count animals in imagery; if you run GPS collars, push tracks into Movebank and analyze them with the amt and ctmm packages in R. Both turn work that used to take weeks of manual counting or hand-drawn home ranges into a repeatable, defensible pipeline.

For modeling and writing, the toolkit is free or low-cost: MaxEnt and Wallace for species distribution models, Google Earth Engine and QGIS for habitat layers, and ChatGPT or GitHub Copilot to write the R and Python. Ground-truth every AI count and protect sensitive-species locations.

The one rule, forever: AI detections, counts, and habitat models are estimates to verify, not field-confirmed fact — a missed or misidentified protected species, or an over-confident density estimate, can misdirect an ESA or NEPA decision you're legally accountable for. Ground-truth AI outputs, confirm sensitive-species calls with expert review, protect the locations of endangered species so data can't enable poaching, follow survey permits and animal-welfare rules, and document model uncertainty in every report.
The plays — exact steps, exact prompts

Do these in order. Each one is copy-paste ready. You do not need to know anything about AI going in.

1
Count wildlife from drones and aerial imagery with AI
Why this pays: Aerial and drone counts of large populations are high-value survey work, and manual image counting is the bottleneck. An AI detector that counts animals across thousands of frames lets you deliver population estimates fast and defensibly — the capability that wins big survey and monitoring contracts.
PicterraRoboflowArcGIS Pro
1
Label a sample of your aerial or drone imagery in Picterra or Roboflow, train a detector to find and count your target species, and bring the results into ArcGIS Pro for density and distribution analysis.
2
Design a defensible AI-assisted aerial count.
Copy-paste this prompt
I'm estimating [a waterfowl population] from [drone imagery over wetlands]. Design a workflow: how much imagery to label to train a reliable detector, how to validate detection accuracy (precision/recall against manual counts), how to correct for missed and double-counted animals, and how to propagate that error into a population estimate with confidence intervals. Note what to report in the methods.
Validate the detector against manual counts on a held-out sample and correct for detection bias — an uncalibrated count over- or under-states the population and can mislead a management decision.
What you'll haveFast, defensible aerial population estimates — the survey capability behind larger monitoring contracts.
2
Reconstruct movement and home range from GPS telemetry
Why this pays: GPS-collar and telemetry analysis underpins the siting, corridor, and impact studies that energy and infrastructure clients pay well for. AI-assisted movement modeling lets you turn raw tracks into home ranges, corridors, and resource-selection results — the technical work that commands consulting rates.
MovebankR (amt/ctmm)ChatGPT
1
Store and clean tracks in Movebank, then model home ranges, movement, and habitat selection in R with the amt and ctmm packages, using ChatGPT to write and debug the code.
2
Get the right movement analysis and defensible code.
Copy-paste this prompt
I have [GPS-collar data from 15 elk, hourly fixes over a year]. I want home ranges, seasonal movement, and a resource-selection analysis relative to [roads, cover, and water]. Recommend the appropriate methods and R packages (e.g., ctmm for autocorrelated home ranges, amt for step-selection), write commented starter code, and explain the assumptions and how to handle irregular fixes and autocorrelation.
AI suggests methods and code; confirm the model fits your data (fix rate, autocorrelation, sample size) and check assumptions before trusting the output — you own the analysis.
What you'll haveHome ranges, corridors, and selection results from raw tracks — the telemetry analysis that wins technical contracts.
3
Model species distribution and habitat suitability for permitting
Why this pays: Environmental impact and siting decisions hinge on where protected species can occur. Species distribution models let you map suitable habitat across a project area and defend it — the analytical deliverable at the core of well-paid EIA and permitting work.
MaxEntWallaceGoogle Earth Engine
1
Assemble environmental predictors in Google Earth Engine, then build and evaluate a species distribution model in MaxEnt or the Wallace R application, mapping habitat suitability across the site.
2
Build a defensible SDM for a permitting context.
Copy-paste this prompt
I need a species distribution model for [a threatened salamander] across [a proposed pipeline corridor] to support an impact assessment. Recommend a workflow in Wallace/MaxEnt: how to source and filter occurrence records, choose and check for correlation among predictors, address sampling bias, evaluate the model (AUC, omission, and a sensible threshold), and communicate uncertainty honestly to a regulator. Flag the assumptions a reviewer will challenge.
SDMs predict potential, not presence — validate against field survey where it matters, never treat suitable habitat as confirmed occupancy, and state uncertainty clearly for any protected-species conclusion.
What you'll haveDefensible habitat-suitability maps for impact and siting work — the analysis that anchors high-value permitting contracts.
4
Turn camera-trap and acoustic surveys into abundance estimates
Why this pays: Camera-trap and acoustic surveys generate mountains of data, and clients pay for the population estimates, not raw images. AI classification plus occupancy and density modeling lets you deliver abundance and occupancy results fast — more billable surveys per season.
Wildlife InsightsBirdNETR (unmarked)
1
Auto-classify camera-trap images in Wildlife Insights and audio in BirdNET, then fit occupancy and density models in R with the unmarked package (or distance sampling) to turn detections into estimates.
2
Move from AI detections to a defensible population estimate.
Copy-paste this prompt
I have [detections of a mesocarnivore from 60 camera-trap stations over 90 days], auto-classified by AI. Recommend how to go from detections to an occupancy or density estimate: the appropriate model in unmarked, how to structure detection histories, which covariates to include, how to account for imperfect and AI-error detection, and how to report the estimate with uncertainty and the AI's verified accuracy.
Manually confirm a sample of AI classifications — especially every rare or protected-species detection — before it enters a model, and report the classifier's error rate in your methods.
What you'll haveOccupancy and density estimates delivered from raw survey data — more billable surveys turned around per season.
5
Predict and reduce human-wildlife conflict
Why this pays: Human-wildlife conflict work — for ranchers, utilities, transport, and municipalities — is a growing, well-funded niche. AI-assisted hotspot analysis and mitigation planning let you deliver a defensible conflict-reduction strategy, a specialized service that commands premium consulting rates.
Google Earth EngineQGISChatGPT
1
Combine incident records with habitat, road, and land-use layers in Google Earth Engine and QGIS to map conflict hotspots and the conditions that predict them.
2
Build a conflict hotspot analysis and mitigation plan.
Copy-paste this prompt
For [vehicle collisions with deer along a 40 km highway], outline an analysis: how to map hotspots from incident data, which landscape and traffic variables to test as predictors, an appropriate model to identify high-risk segments, and an evidence-based mitigation plan (crossings, fencing, signage, timing) prioritized by cost and expected effectiveness. Note what field verification and stakeholder input are required.
Ground the analysis in verified incident data and local field knowledge — a mitigation recommendation affects budgets and safety, so validate hotspots on the ground before advising.
What you'll haveA defensible, prioritized conflict-reduction strategy — a specialized service that commands premium rates.
6
Draft NEPA, ESA, and monitoring reports faster
Why this pays: Consulting wildlife biology runs on reports: biological assessments, NEPA documents, monitoring reports. AI that accelerates drafting and synthesis lets you turn projects around faster and take on more, directly increasing billable output — the core of consulting income.
ClaudeNotebookLMPerplexity
1
Load your survey data, agency guidance, and species accounts into NotebookLM, then use Claude to draft report sections grounded strictly in those sources, checking regulations with Perplexity.
2
Draft a defensible impact-assessment section.
Copy-paste this prompt
Draft the [effects analysis] section of a biological assessment for [a solar development overlapping potential habitat for a listed bird]. Structure it by effect pathway (habitat loss, disturbance, collision), summarize likely direct and indirect effects during construction and operation, and propose standard avoidance, minimization, and mitigation measures. Flag every place where my site-specific survey data must replace a placeholder before this is final.
Every effect conclusion must rest on your actual survey data and professional judgment, not AI assumptions — and a wrong call on a listed species carries legal liability.
What you'll haveAssessments and monitoring reports drafted faster with your data on top — more projects delivered, the core of consulting revenue.
Your 12-month sequence to the top of the range

How the plays above stack into a path from median pay toward the $177,760 tier.

Month 1
Attack your biggest data bottleneck — aerial counts in Picterra or telemetry in Movebank/R — with a validation step built in.
Months 2-3
Add species distribution modeling (MaxEnt/Wallace) and adopt an AI coding partner for your R and geospatial work.
Months 3-6
Turn camera-trap and acoustic surveys into occupancy/density estimates and speed report drafting with AI.
Months 6-12
Develop a specialization (telemetry, conflict, or SDM-based permitting) and package an AI-accelerated survey-to-report workflow.
Year 2
Target senior consulting or project-lead roles where your AI-scaled surveys and modeling command pay at the top of the range.
Gear for this job

As an Amazon Associate, PayCrunch earns from qualifying purchases. Links to books and tools are for the job on this page; we only recommend what we’d use in the work.

Bolstad GIS Fundamentals, 7th

Same live Bolstad 7th already on gis-analyst / forest-ranger / cartographer / hydrologist / park-ranger / archaeologist (ASIN 0971764751). This leftover page’s play 3 is Model species distribution and habitat suitability for permitting; tools name ArcGIS Pro / Google Earth Engine / MaxEnt; Months 2–3 is Add species distribution modeling and adopt an AI coding partner for R and geospatial work. GIS fundamentals text for leftover ArcGIS / habitat-model / geospatial work — not leftover GISP as a card. Confirm 0971764751. Live page HTTP 200, no PC_GEAR / amazon.com/dp / tag=paycrunch-20 at 2026-09-18 12:17 AM PT. Source page: forest-ranger.

Next steps for a Wildlife Biologist

Some links below are affiliate or partner links. PayCrunch may earn a commission if you enroll or subscribe through them, at no extra cost to you. Wage figures on this page still come from the Bureau of Labor Statistics, not from these programs.

Wildlife Biologist work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Zoologists and Wildlife Biologists (SOC 19-1023). O*NET Job Zone 4 is typical: a bachelor's degree, so the honest next credential is a professional certificate or bachelor's-level coursework — not a random catalog dump.

The occupation's listed knowledge areas include Biology and Geography; the links search those subjects, not a generic 'career courses' list.

Wildlife Biologists in this dataset list ESRI ArcGIS software among the tools in use, so a program that names that stack is a better fit than a survey course.

Biology programs on Coursera for Wildlife Biologist work

Coursera search for biology — a professional certificate or bachelor's-level coursework that lines up with science, not a generic professional-development aisle.

Biology courses on edX

edX search for biology, aimed at science (SOC 19-1023). Same field as the Coursera link, different university catalog.

Screened remote and flexible Wildlife Biologist listings on FlexJobs

FlexJobs screens remote, hybrid, freelance, and flexible listings so you are not wading through unverified ads. This is a job-board search for Wildlife Biologist work, not a claim that they list a counted SOC 19-1023 inventory.

Build a Wildlife Biologist resume on Resume Now

Write a Wildlife Biologist resume, or one aimed at Biological Scientists, All Other, instead of a blank template. Resume Now is a resume builder; we are not claiming a counted template set for this SOC.

Build a Wildlife Biologist resume on Zety

A Wildlife Biologist resume that names the actual tasks on this page, or the step-up title Biological Scientists, All Other, beats a blank template when you apply.

What Wildlife Biologists earn by state

These are the Bureau of Labor Statistics’ own figures for Zoologists and Wildlife Biologists, state by state — not a cost-of-living adjustment applied to the national number. Only states employing at least 500 people in the occupation are shown, because a state median drawn from a handful of workers is noise rather than a signal.

California
$98,530
highest of them · +28% vs the national median
Texas
$49,100
lowest of the 8 states that qualify · -36% vs the national median
The same job pays $49,430 more a year at the median in California than in Texas — 101% higher. That gap is what the Bureau measured, before any question of what it costs to live in either place. California also carries the top of this job’s range, $177,760 — the figure quoted at the head of this page.
California$98,530Alaska$90,370Oregon$85,150Washington$83,780Colorado$83,300Minnesota$68,620Florida$52,750Texas$49,100

Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025, SOC 19-1023. 8 states clear the 500-employee reporting floor for this occupation; those below it are left out rather than shown with a wide error band.

Free data. Use any of it.

PayCrunch publishes verified, BLS-sourced salary + AI-playbook data on 1,000+ professions — free, no signup.

Frequently asked
Will AI replace wildlife biologists?
No. Someone has to design the survey, handle animals ethically, verify a protected-species call, and take legal responsibility for a NEPA or ESA conclusion. AI counts and classifies and models, but it can't do fieldwork or be accountable for a management decision. The biologists who use it survey and report faster; the real risk is a competitor who delivers more, not the software.
Can I trust AI counts, IDs, and habitat models?
Only after verification. Detectors and classifiers have real error rates that spike on rare or cryptic species, and distribution models predict potential, not presence. Ground-truth against field data, manually confirm every protected-species detection, correct counts for detection bias, and report uncertainty. AI output is a hypothesis to verify, never a field-confirmed record.
Wildlife biologist, zoologist, ecologist — does AI help them differently?
Yes. A wildlife biologist's highest-value AI leverage is population and habitat work at landscape scale — aerial counts, GPS telemetry, distribution models, and conflict analysis for permitting and management. That management- and regulation-facing modeling is where consulting pay concentrates, and it's what most directly moves a wildlife biologist toward the top of the band.
How does AI actually increase a wildlife biologist's pay?
By raising throughput and unlocking higher-value work, which matters most in consulting where pay is highest. Automated counting and telemetry analysis let you deliver more surveys; distribution models and conflict analysis win bigger, more technical contracts; faster reporting means more billable projects. More and larger projects is the path to the $177,760 tier.
Which AI tool should a wildlife biologist learn first?
Match it to your biggest bottleneck: Picterra or Roboflow for aerial/drone counts, Movebank and R for telemetry, Wildlife Insights for camera traps. Then add an AI coding partner for your R and geospatial analyses, since that unlocks rigor across every project, and MaxEnt/Wallace when you need habitat modeling for permitting.
Methodology & sources
  • Salary (median, 10th, top of the range) — U.S. Bureau of Labor Statistics, OEWS.
  • By state — the Bureau of Labor Statistics’ own state medians, limited to states employing at least 500 people in the occupation. No cost-of-living arithmetic is applied to a wage anywhere on this page.
  • The plays — PayCrunch's own step-by-step guidance using publicly available AI tools. Tool names/URLs are real and current as of August 2026; prompts written to work as-is. Verify any professional output before relying on it.

Sources