Swift Enduro v Reconyx HF2X. Head to head study looking at large scale camera deployment costs

Findings from a recent study – Monitoring biodiversity on a budget: Optimizing camera trap selection in a biodiversity hotspot. Harry Moore, Darcy Watchorn, Hannah Kilian

Conservation Science and Practice: Volume 7, Issue 12

December 2025

https://doi.org/10.1111/csp2.70192

Designing a wildlife monitoring program is tough enough without having to agonise over gear choices. Do you spend big on premium camera traps, or stretch the budget further with more affordable units? A recent study from the Northern Jarrah Forest in south‑west Western Australia gives a clear, data‑driven answer – and it might surprise you.  Here, they compared the performance of two of the most widely used and commercially available camera trap units, the Swift Enduro and the Reconyx HF2X.

While high-end Reconyx cameras detected slightly more animals per night (< 3% across all species), the more affordable Swift Enduro cameras proved far more cost-effective when viewed at the scale that matters: running a landscape-level monitoring program with enough statistical power to reliably detect changes in wildlife populations over time.

Statistical power, not just pictures

Ecological monitoring using camera traps isn’t just about getting nice photos; it’s about detecting change with confidence. Statistical power – the ability of the monitoring program design to detect a genuine change – is what determines whether managers can detect a drop in occupancy before it’s too late. Poorly designed programs can quietly burn through budgets while missing important trends.

Camera traps have become a workhorse tool for vertebrate monitoring across the world. But the explosion of different camera models, from budget to premium, has made one design choice critical: which camera model actually gives you the best statistical power in large scale monitoring programs and hence the best conservation return per dollar?

Inside the study: 180 sites, two camera models

The study was conducted across 180 sites in the Northern Jarrah Forest, a globally recognised biodiversity hotspot dominated by jarrah and marri forest and spanning strong gradients in fire history and rainfall. At each site, researchers deployed two cameras 100 m apart: one Reconyx HF2X HyperFire and one Swift Enduro, both mounted 30 cm above the ground and baited with peanut butter lure to standardise conditions.

Cameras ran continuously for at least 30 nights focusing on six mammal species.  In total, the survey recorded 5605 animal detections from 75 species, with western grey kangaroos, western brush wallabies, small mammals and echidnas among the most frequently detected species.

Detectability: premium vs budget in the field

On a per‑camera basis, Reconyx did come out ahead. Reconyx units recorded more animal photos per night than Swift and had slightly higher nightly detectability for most of the focal species and functional groups.

When species were grouped into functional types, Reconyx again had higher detectability for all but large terrestrial species, but the differences were only a few percentage points. Large terrestrial species had the highest detectability overall, while small and medium terrestrial species were harder to pick up, reflecting a mix of body size, ecology and movement behaviour.

So if you’re only looking at “Which camera detects more animals per night?”, Reconyx seems to win – but that’s not the question that matters most when budgets and long‑term monitoring are on the line.

Where it really counts: statistical power and program cost

The authors went a crucial step further by asking: given these detectability estimates, how many sites – and how much money – does it take to build a monitoring program with 80% statistical power to detect a 30% decline in occupancy over time?

Two big findings stand out:

1. You need a lot of sites.
   Because nightly detectability for most species was under 10%, achieving high power typically required 100–130 sites, even for relatively common species.
2. Swift Enduro wins clearly on cost at program scale.
   Using typical Australian prices – $690 AUD per Reconyx HF2X HyperFire versus $300 AUD per Swift Enduro – every single optimized monitoring design was cheaper with Swift cameras, even when Reconyx had higher detectability.

Across focal species, Swift‑based designs required an average of 110 sites and cost about $32,900 AUD, while equivalent designs using Reconyx averaged around $61,200 AUD – a difference of roughly $28,000 AUD.

When the team looked at functional species groups, the pattern was the same: Swift consistently delivered the lowest total program cost, with savings on the order of $37,000–$39,000 AUD compared with Reconyx for typical designs.

What this means for practitioners

The study delivers a clear message: when designing broad‑scale camera trap monitoring, you shouldn’t select hardware based solely on detectability or image quality. The key is the cost of achieving the desired statistical power, given realistic detectability and deployment lengths.

Some practical take‑aways:

• Small gains in detectability do not automatically translate into better monitoring outcomes if the cameras are much more expensive.
• At the scale of 100+ sites, per‑unit price differences compound into tens of thousands of dollars in total program cost.
• For most species and functional groups in this study, Swift Enduro cameras allowed managers to reach the same power targets at roughly half the total equipment cost compared with Reconyx.

For agencies responsible for multiple threatened species programs, these differences could scale to hundreds of thousands of dollars saved over time – funding that could support more sites, more years of monitoring or additional conservation actions.

Important caveats and real‑world considerations

The authors deliberately kept their comparison narrow, focusing on detectability and equipment cost. Several other factors matter in real‑world operations:

• Durability and features: The study did not compare sturdiness, failure rates, configuration flexibility or long‑term performance in harsh conditions – all of which can influence lifecycle cost.
• Logistics and maintenance: Field time, travel, and replacement costs were not built directly into the power analysis, although the authors suggest that similar numbers of sites and nights between models mean these additional costs are unlikely to change the overall conclusion.

Despite these caveats, the magnitude of the price difference per unit means that even if cheaper cameras need to be replaced more often, they are still likely to deliver more cost‑effective monitoring in many scenarios.

Key lessons for designing cost‑effective monitoring

This study from the Northern Jarrah Forest offers a simple but powerful framework for anyone planning or revising a camera‑based monitoring program:

• Define the level of change of occupancy you need to detect (for example, 30% occupancy loss) and the statistical power you require.
• Estimate or pilot detectability for your focal species and camera options.
• Model how many sites and nights you need for each camera model to achieve your power target.
• Compare total program cost, not just per‑unit price or per‑camera detectability.

Done this way, camera selection becomes a transparent, quantitative decision rather than a brand or specification preference. For the species and conditions tested in this study, that process pointed clearly to Swift Enduro cameras as the more economical choice for high‑powered, large‑scale monitoring – even when they detected slightly fewer animals per night than their premium counterparts.