The sixth All India Tiger Estimation — the world’s largest wildlife monitoring exercise — is underway as this edition goes to press. For the first time in the survey’s two-decade history, AITE 2026 is measuring not just how many tigers India holds but what kind of ecological system is sustaining them: habitat quality indices, weed infestation burden, corridor functionality scores, disease surveillance parameters, and individual identity through a new three-dimensional stripe-pattern technology. NSH takes you inside the science of counting India’s tigers — what the methodology measures, what it still cannot, and why the seventh census may need to measure things the sixth cannot yet imagine.
The All India Tiger Estimation is, by the standards of any scientific discipline, a remarkably young methodology. The first cycle in 2006 represented a fundamental paradigm shift in Indian wildlife science — abandoning the pugmark census that had been used since 1972, which counted plaster casts of tiger footprints and was subsequently revealed to have systematically and severely overestimated populations, in favour of camera-trap-based occupancy modelling combined with statistical distance sampling for prey density. The 2006 census returned a population estimate of 1,411 tigers with a lower confidence bound of 1,165 — a figure more than 40 percent below the pugmark census’s previous estimate of approximately 3,500, and a number that shocked India into the conservation emergency that ultimately produced the recovery now underway.
Each subsequent cycle has introduced significant methodological advances. The 2010 cycle expanded the camera-trap grid and formalised double sampling — the integration of sign-based occupancy surveys with photographic Capture-Mark-Recapture to estimate total population from sampled individuals. The 2014 cycle applied full CMR methodology across all reserves simultaneously for the first time, producing a statistically robust comparison across the entire reserve network. The 2018 cycle deployed 26,800 camera traps — then the largest wildlife camera network ever assembled for a single survey — and introduced AI-assisted stripe-pattern recognition that began automating individual tiger identification from camera images. The 2022 cycle scaled further to 32,803 cameras at 175 sites across 19 states, generating 97,399 tiger photographs and identifying 3,080 unique individuals, and introduced systematic weed infestation mapping and early climate resilience indicators as habitat quality parameters.
AITE 2026, the sixth cycle, marks two decades of this methodology and represents its most ambitious expansion yet — in geographic scope, in the depth of ecological parameters measured, in the technology deployed for individual identification, and in the range of biological threats it is designed to detect. It is also the first census conducted with the explicit scientific understanding that the tiger population it will enumerate is, in significant measure, living in landscapes outside the formal reserve network that previous cycles were primarily designed to survey.
ALL INDIA TIGER ESTIMATION — METHODOLOGICAL EVOLUTION (2006–2026)
| Census Cycle | Key Methodology Innovation | Population Estimate | Reserves Covered |
| AITE 2006 | Occupancy modelling replacing pugmark census; first camera traps at 9 sites | 1,411 (lower bound) | 17 |
| AITE 2010 | Expanded camera-trap grid; statistical double sampling introduced | 1,706 | 18 |
| AITE 2014 | Capture-Mark-Recapture across all reserves; GIS-based landscape assessment | 2,226 | 22 (in 18 states) |
| AITE 2018 | 26,800 camera traps; AI-assisted stripe pattern recognition; first ecosystem-scale coverage | 2,967 | 50+ |
| AITE 2022 | 32,803 cameras; 175 sites; 97,399 tiger photos; 3,080 individual IDs; weed infestation mapping; climate indicators introduced | 3,682 (avg) | 53 (in 18 states) |
| AITE 2026 (ongoing) | 3D stripe-pattern modelling; zoonotic disease surveillance integration; expanded outside-reserve coverage; 40,000+ cameras; scat DNA + CMR; report due 2027 | TBA — 2027 | 58 + non-reserve forest divisions |
Sources: NTCA official census reports, WII AITE technical volumes, Tribune India (August 2025), BigCatsIndia.com (April 2026). AITE 2026 final report expected July 2027.
AITE 2026 is the first census conducted with the explicit scientific understanding that a significant proportion of India’s tigers live outside notified reserves — and its methodology has been expanded accordingly, extending coverage to private estates and non-reserve forest divisions for the first time at national scale.
The Field Architecture of AITE 2026
AITE 2026 formally commenced on 6 January 2026 at the ThanthaiPeriyar Wildlife Sanctuary in Tamil Nadu’s Erode Forest Division — the first state to initiate Phase I field operations in the sixth cycle. Different states and forest divisions conducted surveys in rolling seven-day windows through January and February 2026, with field data submission deadlines set at 31 January 2026 for the first wave of states. The national field operation involves over 60,000 forest department personnel and trained volunteers — making it the world’s largest single-species monitoring exercise by human deployment.
The survey’s four-phase architecture reflects the progressive refinement of measurement methodology from qualitative sign recording through quantitative population estimation. Phase I, the ground survey, deploys trained forest guards along pre-defined beat transects of approximately 15 kilometres per guard over three days. Using the M-STrIPES mobile application — the GPS-enabled digital recording platform that has been the survey’s data backbone since 2010 — field staff record five categories of data simultaneously: carnivore sign encounters (pugmarks, scat, claw marks, prey remains) on dedicated Form 1; ungulate abundance using distance sampling methodology on line transects, Form 2; vegetation structure including canopy cover, tree and shrub composition, and critically, weed infestation indices on Forms 3A and 3C; livestock and human activity indicators on Form 3B; and dung counts in systematic 40 square-metre plots on transects, Form 4. All data entries are geo-tagged through the phone’s GPS receiver and uploaded to the NTCA-WII centralised data platform in near real time.
In Telangana, Phase I field operations conducted between 19 and 25 January 2026 covered 3,053 forest beats statewide, involving 4,500 forest department personnel and approximately 1,700 volunteers. The survey extended across tiger reserves, wildlife sanctuaries, reserve forests, and other forest areas in 32 districts. Early results recorded 994 tiger and carnivore signs across Telangana’s forest landscape — a figure that represents indirect evidence of presence rather than confirmed individual counts, which emerge from the subsequent phases. Phase II integrates satellite remote sensing data — processed through the Wildlife Institute of India’s geospatial analysis platform — with the Phase I ground data to generate habitat quality maps and identify optimal camera placement zones. Vegetation indices derived from satellite imagery, combined with human disturbance parameters and water source mapping, produce the spatial framework for Phase III’s camera deployment.
Phase III: Camera Traps, Individual Identity, and 3D Stripe Modelling
The third phase is the methodological centrepiece of AITE and the one that has evolved most dramatically across cycles. AITE 2026 is deploying over 40,000 camera traps — exceeding the 2022 cycle’s 32,803 — across a 4 square-kilometre grid system in which each grid cell contains two motion-sensitive cameras positioned to capture both flanks of passing animals. Camera placement priorities are determined by Phase II habitat mapping, with priority given to trail intersections, ridgelines, waterholes, and territorial marking sites identified during Phase I sign surveys. Cameras are activated for 25 to 60 days depending on site accessibility and estimated tiger density, with activation windows timed to coincide with peak movement periods.
Individual tiger identification — the critical analytical step that converts camera images into population estimates through Capture-Mark-Recapture statistics — has undergone its most significant technical advance since AI-assisted 2D stripe recognition was introduced in 2018. AITE 2026 has introduced software capable of generating three-dimensional models of tiger stripe patterns from two-dimensional camera images, substantially improving identification accuracy for tigers photographed at angles or in lighting conditions that previously caused misidentification or unidentified status. The principle underlying this advance is the same as 2D recognition — that each tiger’s stripe pattern is unique and individually diagnostic, equivalent to a human fingerprint — but 3D modelling extrapolates the full pattern from partial views, reducing the proportion of images classified as unidentifiable and improving the precision of CMR population estimates.
Field staff in AITE 2026 have received specific training in scat collection for DNA analysis — Phase IV’s contribution — with emphasis on collection technique, preservation protocol, and the importance of collecting samples that complement rather than duplicate camera-identified individuals. DNA extraction from scat provides two scientific functions simultaneously: individual identification of tigers that camera traps did not capture, extending the census’s detection probability beyond the camera grid’s spatial coverage; and genetic data contributing to the WII-NTCA national tiger DNA database, adding to the longitudinal genetic record that enables population connectivity and inbreeding analysis.
What AITE 2026 Is Measuring for the First Time at This Scale
The most scientifically significant advance in AITE 2026 is not the technology — the cameras, the 3D software, the expanded M-STrIPES deployment — but the expanded parameter set. AITE 2022 introduced weed infestation indices and early climate change resilience indicators as data fields in the M-STrIPES survey forms. AITE 2026 has embedded these parameters more systematically, making habitat quality assessment — not just tiger counting — an explicit co-primary objective of the exercise.
The invasive species burden measurement in AITE 2026 records lantana camara, Eupatorium, Parthenium, and other invasive weed coverage as a vegetation parameter on every transect, using standardised cover-class estimation. This generates, for the first time at national scale, a spatially resolved map of invasive species burden across India’s entire tiger-bearing forest landscape — not just within reserves but across the non-reserve forest divisions now included in the survey’s Phase I coverage. The scientific value of this dataset is considerable: it enables conservation biologists and reserve managers to correlate invasive species burden with prey density data, tiger sign frequency, and camera-trap capture rates from the same survey cycle, providing the first integrated national evidence base for the relationship between lantana invasion and tiger habitat functionality that Article 2 of this package has explored in depth.
The corridor connectivity parameters in AITE 2026 extend the survey’s reach explicitly into the matrix habitats between reserves — the forest divisions, community forests, and private estates where tigers must travel if population connectivity is to be maintained. Tamil Nadu’s AITE 2026 protocol explicitly includes coverage of ‘buffer zones, tiger-bearing corridors, and even private estates where tigers have been documented.’ This expanded coverage directly addresses the scientific gap that landscape genetics studies (Article 4) have identified: that corridor functionality cannot be assessed from reserve-boundary data alone, because the gene flow that makes corridors biologically meaningful occurs in the spaces between reserves that previous census cycles inadequately sampled.
AITE 2026: THE SCIENCE IN NUMBERS
▸ 6 January 2026 — official start date (ThanthaiPeriyar WLS, Tamil Nadu)
▸ 60,000+ — forest staff and trained volunteers deployed nationally
▸ 40,000+ camera traps — exceeding AITE 2022’s 32,803
▸ 4sq km grid cells — each with two cameras, covering both flanks
▸ 25–60 days — camera activation windows per site
▸ 3,053 forest beats — covered in Telangana alone (Jan 19–25)
▸ 4,500 forest staff + 1,700 volunteers — Telangana Phase I deployment
▸ 994 tiger/carnivore signs — Phase I Telangana preliminary returns
▸ 3D stripe-pattern modelling — new tech advance over 2022’s 2D AI identification
▸ NEW: Zoonotic disease surveillance parameters integrated for first time
▸ NEW: Private estate and non-reserve forest division coverage at national scale
▸ NEW: Systematic invasive species burden index on all transects
▸ July 2027 — expected release of consolidated national findings
The Zoonotic Dimension: Disease as a New Census Variable
One of the most consequential scientific expansions in AITE 2026 is the integration of wildlife disease surveillance as a monitoring dimension — an addition prompted by research published in NTCA’s own in-house scientific publication Stripes and conducted by researchers from the National Centre for Biological Sciences (NCBS) and the Tata Institute of Fundamental Research (TIFR). The research documented that canine distemper virus, rabies, Nipah virus, and bovine tuberculosis are no longer outliers in India’s tiger landscapes but ‘creeping realities’ — pathogens that have been confirmed in co-predators and prey species living in proximity to tigers and that represent a growing threat to tiger population viability.
The scientific basis for concern is well established in global conservation medicine. Canine Distemper Virus (CDV) — transmitted primarily through domestic dogs that range into forest-fringe areas and, in some landscapes, through feral dog populations that have established inside reserve buffers — has caused documented population crashes in large felids. The 1994 CDV outbreak in Serengeti lions killed approximately 30 percent of the population. In India, CDV has been detected in carnivore populations in multiple tiger landscapes, and feral dogs were recorded by camera traps in most of India’s 50 reserves surveyed in the 2018 Status of Tigers report. Bovine tuberculosis (Mycobacterium bovis), which has been documented in leopards and lions internationally and has a confirmed spillover pathway through cattle grazing in reserve buffer zones, was specifically cited by the NCBS-TIFR researchers in NTCA’s Stripes: ‘We already have documented cases of tuberculosis in leopards and lions. Our tiger monitoring teams must be trained not just in stripe-based individual identification but also in collecting and handling biological samples, reporting abnormalities in behaviour, and understanding landscape-level disease predictors.’
AITE 2026 is, for the first time in the census’s history, training field personnel to record abnormal wildlife behaviour as a systematic data field, and to collect biological samples — in addition to DNA-identification scat — from carcasses and visibly sick animals encountered during Phase I surveys. The integration of disease surveillance into the census infrastructure is scientifically efficient: it deploys the same network of thousands of trained field observers who are already covering the landscape systematically, using standardised protocols, to generate a first-pass disease surveillance dataset at a spatial scale that dedicated disease monitoring programmes cannot match. The data quality is necessarily preliminary — Phase I field observers are not wildlife veterinarians — but the spatial coverage and the standardisation of reporting are scientifically valuable in ways that opportunistic case reports cannot achieve.
What AITE 2026 Still Cannot Measure — and Why This Matters
Scientific honesty requires naming what the world’s most sophisticated wildlife monitoring exercise still cannot tell us. The CMR-based population estimate that will emerge from AITE 2026’s analysis in 2027 will be more accurate than any previous Indian tiger count — but it will still be an estimate, with confidence intervals that reflect the statistical limitations of detection probability modelling across a survey area of over 400,000 square kilometres. The lower bound of the 2022 estimate (3,167) differed from the average (3,682) by more than 500 animals — a gap that represents real scientific uncertainty about a population whose total numbers are still relatively small in the context of species-level viability.
More fundamentally, the population estimate tells us how many tigers exist at a point in time. It does not tell us whether the ecological system supporting them is stable, improving, or degrading. A census number of 4,000 tigers in 2027 would be consistent with either a thriving, ecologically secure population in high-quality habitat with functional corridors, or a numerically large but genetically fragmented population in degraded habitat with declining prey density, high conflict incidence, and accumulating inbreeding depression. The habitat quality parameters that AITE 2026 is measuring — invasive species burden, prey density, corridor connectivity indicators, vegetation structure — are designed to begin addressing this gap, but their analysis and integration into the headline finding will require the same rigorous scientific commitment that the population estimate receives.
The genetic dimension remains the most significant measurement gap. AITE 2026’s Phase IV scat DNA sampling generates individual identification and contributes to the national genetic database — but it does not constitute a systematic genetic diversity survey. The microsatellite and mitochondrial DNA analyses that have revealed the Tiger Genetic Blocks, source-sink dynamics, and corridor functionality in landscape genetics studies (Article 4) are conducted as separate research projects by WII and partner institutions, not as formal AITE outputs. The sixth census report in 2027 will not include a national genetic diversity assessment alongside its population estimate. That gap — between the number of tigers and the genetic health of those tigers as a population — is the frontier that India’s seventh tiger estimation cycle, due around 2030, may need to formally close.
The zoonotic disease surveillance dimension is similarly preliminary in AITE 2026. Recording abnormal behaviour and collecting opportunistic biological samples during Phase I surveys is a scientifically sound first step, but it is not the systematic serological and pathogen-load survey that wildlife epidemiologists would specify for a rigorous national disease risk assessment. Building this capacity into future census cycles — through partnerships with veterinary research institutions, standardised sample collection and cold-chain protocols, and analytical laboratory capacity at WII — is the scientific infrastructure investment that the NCBS-TIFR researchers are calling for in Stripes.
The 2027 Report: What India Should Expect — and Ask
When the AITE 2026 national findings are released in 2027, the headline number will again command global attention. It will almost certainly be larger than 3,682 — the trend is clear, the trajectory is sustained, and the addition of private estates and non-reserve forest divisions to the survey’s coverage alone will capture animals previously uncounted. The number will be reported as a conservation success, and in many respects it will be one.
NSH’s scientific recommendation to its readers, based on the analysis in this package, is to read the 2027 report’s full technical volume — not the press release — and ask six specific scientific questions of the data it contains. What is the habitat quality index for each reserve and forest division, and how does it correlate with the invasive species burden measured in Phase I? What are the prey density estimates for each landscape, and where have they declined since 2022? What does the corridor connectivity mapping show about which corridor segments are being used and which are functionally blocked? What is the geographic distribution of the population increase — is it concentrated in the same six high-performing reserves that dominated the 2022 figures, or is it more evenly distributed? What does the disease surveillance data show about pathogen presence across landscapes? And what is the 2027 census’s own assessment of measurement uncertainty — not just the confidence intervals around the headline number, but the scientific team’s evaluation of what the expanded parameter set still cannot capture?
The AITE is a living scientific instrument, refined over twenty years and six cycles. Its evolution from pugmark plaster casts to 3D stripe-pattern modelling to disease surveillance integration is a remarkable scientific story in its own right — a story of methodology improving to meet the conservation challenge rather than resting on the headline number it produces. The seventh cycle, due around 2030, will have AITE 2026’s habitat quality data as its baseline. Whether it also has a genetic diversity baseline, a systematic disease load assessment, and a more complete picture of India’s outside-reserve tiger population will depend on the scientific investment made between now and then. The counting is the beginning. The science behind the count is what determines whether India still has tigers worth counting in 2050.
- Dharan shah
