Tāmaki Paenga Hira Auckland Museum’s pest monitoring tool has evolved from a basic spreadsheet into a museum-wide system with automated functions, visual summaries, and standardised metrics for analysing pest activity. Designed for accessibility and local relevance, the tool supports cross-departmental collaboration and strengthens preventive conservation practices. This article reflects on the cultural shift that occurs when pest management becomes a shared responsibility — supported by user-friendly data and broad staff engagement. Drawing on international Integrated Pest Management (IPM) best practice, we offer an Aotearoa-specific case study showing how global standards can be successfully adapted to local museum contexts.

Preventive conservation, museums, collection care, data analysis, pests

Museum collections — particularly organic materials such as textiles, paper, leather, and wood — are vulnerable to pest damage. Insects, rodents, and microorganisms represent the majority of pests that are capable of damaging or destroying material culture (Strang and Kigawa 2009). To mitigate this, Auckland Museum follows an Integrated Pest Management (IPM) approach to protect our collections. IPM is an integral part of preventive conservation; it focuses on proactively managing pest risks through environmental controls such as maintaining stable temperature and relative humidity, managing physical barriers, and ensuring good building maintenance, alongside systematic monitoring, reducing the need for dangerous pesticides.

As part of our IPM strategy, we implement a range of preventive measures, including quarantine protocols for incoming objects, freezer and anoxic treatments, designated food and drink zones, strict housekeeping, and staff training. These efforts are supported by the cross-department pest committee, the Bug Busters, which fosters collaborative action and staff engagement. While IPM is a shared responsibility across the museum, it is spearheaded and overseen by a Collection Care staff member, ensuring consistent leadership and accountability.

Preventive conservation relies on evidence-based decision-making and data underpins action (Querner 2015). For IPM at Auckland Museum, this data comes primarily from our monthly monitoring programme. A network of strategically placed sticky monitors¹ are distributed throughout collection stores, galleries, offices, and kitchen areas. Each month, trained collections staff check and replace these monitors, identifying any insects present and recording findings in our custom Excel-based monitoring spreadsheet. With over 170 monitors checked monthly, we generate large volumes of data which must be interpreted meaningfully.

Initially designed as a simple record keeping tool, our monitoring spreadsheet has evolved into an effective data analysis tool with automated functions, visual summaries and includes the Pest Occurrence Index (POI). The POI is a standardised metric for contextualising pest activity, discussed in more detail below. Importantly, our approach demonstrates that effective pest monitoring doesn’t require expensive software or advanced digital skills. Using a familiar tool, Microsoft Excel, we’ve developed an accessible system that suits the needs of our museum and encourages participation across departments.

Our in-house adapted pest monitoring tool enables more targeted and informed responses by helping us detect trends, assess risks, and advocate for timely interventions. This article explores the development and impact of our monitoring spreadsheet as a case study in how locally adapted, data-driven tools can enhance pest management practices in line with international preventive conservation standards.

Recognising a gap in Aotearoa-specific IPM literature, this article invites sector-wide sharing and collaboration. Building on previous work exploring the social and institutional dynamics of IPM implementation, we reflect on how our approach has enhanced pest risk analysis and socialised IPM practice across teams, making pest data more visible, actionable, and embedded in everyday museum work (Miller 2019). In doing so, we hope to encourage greater knowledge-sharing and help foster a more connected IPM network within Aotearoa’s GLAM community.

When a pest monitoring programme was first introduced at the Museum, an Excel spreadsheet was set up as a record-keeping tool to capture insect counts from the monthly checks. Collections staff from different departments were rostered on to check the sticky monitors in their assigned areas — mainly collection stores — and enter their findings into the shared spreadsheet. As the dataset grew, it became clear that regular monitoring was an effective way to detect pest presence, identify patterns, and assess risks to collections.

As the Collection Care team’s knowledge and expertise in IPM grew, the scale and scope of the monitoring programme steadily expanded to encompass a broader cross-section of museum spaces. In addition to collection stores and galleries, sticky monitors were deployed in back-of-house areas, staff offices, and kitchen spaces. As these spaces evolved — for example, due to gallery renewals, temporary exhibitions, or expanding collection storage — the number and placement of the sticky monitors were adjusted accordingly.

This expanded monitoring was driven partly by a more holistic understanding of pest behaviour and the environments in which they thrive, and it was also shaped by our recognition that robust, building-wide data is essential for advocating for infrastructure improvements. Such improvements have included maintaining a tightly sealed building envelope with the installation of brush seals on all doors; installing adhesive mats at the entrances to back-of-house collection areas as a way to reduce dust and debris and to evaluate effective housekeeping; and enhancing HVAC systems to prevent fluctuations. As Pinniger (2015) notes, long-term IPM success depends on continuous monitoring and the effective use of data to inform and influence at higher, decision-making levels.

As the monitoring programme became more comprehensive and the volume of data increased, several new challenges emerged. A key issue was the inconsistent recording of insect identifications; terms like ‘psocid’ and ‘booklice’ were used interchangeably, and well-intentioned descriptors like ‘juvenile’ or ‘big’ silverfish, introduced further discrepancy. Irregular monitoring intervals also posed problems as checks were occasionally delayed or missed. Additionally, the more sticky monitors that we deployed, the more insects we found. This increase in raw counts did not necessarily reflect a worsening pest situation but simply reflected the expanded monitoring area. These inconsistencies introduced potential bias into the data, making interpretation less reliable.

These challenges reflect a broader issue identified in IPM literature; pest data is only as effective as the way it is recorded, contextualised, and communicated. As Baars and Henderson (2019) argue, well-intentioned IPM programmes can quickly become unusable or overlooked if data is inconsistently formatted or difficult to interpret. They emphasise the importance of standardised terminology, regular monitoring intervals, and clear visualisation tools to make pest data more actionable for varied audiences from decision-makers to operational staff. These insights highlighted the need for a standardised approach for recording data and more effective methods for interpreting and communicating pest activity in meaningful, contextual ways.

The earliest version of our pest monitoring spreadsheet was a basic table used solely to log insect counts (Fig. 1); it provided a useful foundation for what would become a much more robust and strategic data tool.

In the current version, each department has a dedicated tab within the Master spreadsheet (Fig. 2). This allows pest checkers to access their specific area directly without needing to filter through a centralised master list, making data entry faster and less prone to error. It also fosters greater ownership and accountability over localised pest monitoring, while simplifying the overall process.

Figure 1. 

Screenshot of the original spreadsheet layout. © Auckland Museum CC BY 4.0.

Figure 2. 

Screenshot of current version of pest monitoring spreadsheet, showing a typical departmental input sheet (some pest columns are hidden). © Auckland Museum CC BY 4.0.

Key identifying information for each sticky monitor such as department, room name or number, monitor ID, and location, is pre-filled and colour coded. These cells are locked to prevent accidental edits and maintain data integrity. Staff manually enter the date of each check, their name, and the status of the monitor (selected from dropdown lists), choosing from standardised terms like “clean,” “pests,” or “missing,”. Insect counts are entered into defined columns grouped into two categories:

• Primary museum pests (e.g., silverfish, booklice), which are known threats to collections
• Secondary insects (e.g., cockroaches, spiders), which may not pose a direct risk but can signal issues like moisture or emerging microclimates

This structure supports consistent data entry, easier sorting, and a risk-based approach to interpretation.

Once data entry is complete each month, a built-in macro automates the transfer of all inputs into a central “All Departments” sheet. This centralizes the data, clears the departmental tabs, and prepares the system for the next cycle. Pivot tables and automated visual summaries, such as charts and conditional formatting, allow staff to quickly review trends, spot anomalies, and target follow-up actions.

Today, the spreadsheet functions as both a data repository and an operational tool. Its design emphasises usability, consistency, and clear communication, enabling Collection Care and other teams to make more informed, timely, and collaborative decisions in line with preventive conservation best practices.

To address the challenges introduced by an increasing number of pest monitors and the resulting bias in raw pest counts, we incorporated the Pest Occurrence Index (POI) into our Excel spreadsheet. Developed by Jane Henderson and Christian Baars, the POI is a standardised metric that calculates pest activity relative to floor area, number of monitors, and exposure time — enabling more accurate comparisons across spaces and over time. (Baars and Henderson 2019). For instance, 10 silverfish collected from five monitors in a large store over a 90-day period may represent a lower risk than finding the same number in a small gallery with just one monitor checked over 14 days. While the pest count is identical, the POI accounts for room size, monitor density, and exposure time, revealing significantly higher activity in the smaller space. This provides a clearer sense of where pest activity is concentrated and helps prioritise areas for targeted follow-up.

Incorporating the POI into our spreadsheet was relatively straightforward as we already collected most of the required data. Floor area measurements were sourced from existing architectural plans and added as a new data field. Using standard Excel formulas, we embedded the POI calculation so that results are automatically generated in the background without requiring any change to staff workflows.

We integrated the POI into our dashboard alongside existing pivot tables and conditional formatting, allowing us to quickly visualise fluctuations, detect emerging issues, and better target pest management response. Because the POI adjusts for room size, number of monitors, and days between checks, it gives a more meaningful picture of pest activity than raw counts alone. A higher POI score reflects a higher density of pests relative to the monitored space and time. For example, a drop from a POI of 7 to 6 over a year signals a measurable improvement and helps to validate the impact of preventive actions or conversely, highlight when further intervention is needed. For more details on how the POI is calculated and integrated, see our technical notes here.

One major advantage of the POI is its ability to be applied retrospectively. Because the formula relies on existing metadata (monitor count, space area, and exposure time), we’ve been able to apply it to historical pest data, adding essential context to older records. What were once raw insect counts can now be interpreted through a standardised lens, helping us identify long-term trends, evaluate past interventions, and track progress more meaningfully over time.

The standardised nature of the POI also enables benchmarking across different sites. This is particularly valuable at Auckland Museum, where a significant portion of the collection is stored at an offsite storage facility. Beyond our institution, the POI allows museums — regardless of their scale or setup — to apply a common formula, facilitating data-sharing and sector-wide comparisons. This supports a culture of collaboration and continuous improvement in preventive conservation.

The refinement of our spreadsheet and the integration of standardised metrics has not only supported routine monitoring but also informed broader preventive conservation strategies across the museum. Through ongoing analysis, we’ve identified risks linked to space functionality, visitor movement, and staff practices allowing us to make more strategic, data-informed decisions in our daily work.

One recurring insight is the elevated presence of indicator species — such as springtails, cockroaches, and drain flies — within public galleries. These insects are often considered “hitchhikers,” entering the building on visitors, bags, or incoming materials. Their numbers typically increase during periods of high foot traffic and/or elevated humidity, especially in the summer months. While not directly harmful to collections, their presence can attract more risky pest species, such as dermestid beetles, which feed on their remains. By anticipating these seasonal increases, we can advocate for enhanced cleaning schedules and greater vigilance during high-risk periods.

We’ve also observed noticeable differences in pest activity based on the function of specific spaces. For example, collection stores tend to show low and stable insect activity, whereas kitchens and office spaces are more dynamic environments, often associated with spikes in pest presence. This variation highlights the influence of human behaviour — especially food consumption and storage — on pest risk. In some cases, we’ve even detected high-risk museum pests, such as Reesa vespulae (Undertaker Carpet Beetle) and Stegobium paniceum (Drugstore Beetle), in our staff break rooms and kitchenette areas. These findings have strengthened our case for designating food zones, discouraging long-term food storage, and centralising staff eating areas to minimise risk near collection spaces.

Our monitoring data has also begun to inform how we think about exhibition risk. Monitors placed in galleries, particularly those with open displays, often capture more insect activity than those placed in sealed environments, suggesting a potential correlation between display type and pest presence. While we are still gathering long-term data to fully understand these patterns, early findings support the case for prioritising sealed displays for vulnerable organic objects and highlight the value of integrating pest considerations into exhibition planning and design.

Data insights have also strengthened our ability to advocate for targeted resources, such as increased cleaning contracts, improved infrastructure sealing, or environmental control upgrades. Being able to demonstrate data-driven need helps secure support from decision-makers, backing up proposals with tangible evidence (Fig. 3).

Collectively, these insights show how pest data, when organised and interpreted effectively, can move beyond record-keeping to actively inform space planning, visitor management, food policies, and exhibition practices. By integrating this evidence into preventive strategies, our spreadsheet directly supports risk management, resource allocation, and policy decisions, fostering more resilient and sustainable museum practices.

Figure 3. 

Comparison of pest activity across room types (April 2025), highlighting variation by function and supporting informed risk management decisions. © Auckland Museum CC BY 4.0.

Encouraging staff engagement and building confidence in pest identification and response has been a key part of our IPM strategy. This approach reflects earlier observations that successful pest management is shaped as much by institutional culture and collaboration as by technical procedures (Miller 2019). By taking the time to educate and train the monthly pest checkers, we’ve fostered an informed and invested team. IPM is not the responsibility of a single department; it relies on a museum-wide commitment to preventive care. Building a shared understanding of pest risks and encouraging collaborative responses has helped shift IPM from a Collection Care task to a museum-wide responsibility.

The spreadsheet itself has played a key role in supporting this collective approach. Its user-friendly layout, automated functions, and visual summaries, such as charts, pivot tables, and POI figures, have made pest data more accessible and easier to interpret, even for staff with basic Excel skills. This has empowered staff outside of Collection Care to regularly engage with the data, becoming more familiar with baseline pest activity and recognising when risks escalate beyond normal thresholds. As a result, we’ve seen improved data literacy across teams, stronger collaboration between departments, and more open communication regarding potential pest issues.

To ensure our monitoring programme remains responsive to evolving risks, we regularly review and adjust our pest monitor placement. We consider factors such as current building use, construction activity, recent pest findings, seasonal shifts and collection movements. Staff insights can play a key role in this process as pest checkers are often first to notice early indicators of change, such as increased dust or environmental fluctuations that may affect pest activity. By fostering open communication and shared ownership, staff across departments contribute valuable observations that help inform our strategy. As a result, the monitoring programme remains adaptable and targeted, supporting a proactive, rather than reactive approach, in line with core IPM principles.

The growth in pest literacy and engagement across departments has been made possible, in part, by the accessibility of our chosen tool. While there are several commercial pest management platforms available, we deliberately chose to develop our system in Microsoft Excel. Excel is widely accessible within our institution, incurs no additional licensing fees, and is familiar to many staff members, reducing the need for extensive training. Its flexibility allows us to tailor the spreadsheet to our evolving needs without relying on external technical support. Importantly, there are also multiple pathways for upskilling in Excel, whether through online tutorials, external training, or in-house expertise. These skills are transferable and support capability-building across teams, extending the benefits of this tool beyond pest management alone.

In contrast, dedicated pest management software can pose challenges. Commercial tools often come with expensive licenses or subscription fees, and their support teams may be based in different time zones. Additionally, some platforms lack local relevance. For example, Conserv, a purpose-built subscription tool for IPM tracking, includes a global pest database which excludes some insects commonly found in Aotearoa. This can cause confusion, as users may encounter unfamiliar species in the database while not finding local pests represented, creating a risk of misidentification. The complexity of such programmes and the need to learn a new system can also deter engagement, particularly for staff with varied technical expertise. Our Excel tool, by comparison, reflects local biodiversity, is maintained in-house, and encourages buy-in and skill-sharing across teams.

While our IPM tool has proven to be highly effective and relevant for our needs, Excel is not without its limitations. There are some challenges we’ve encountered which are inherent to working within a spreadsheet-based system. These include:

• Risk of formatting errors and reduced stability of automated processes due to the use of data ranges instead of structured Tables.
• Non-flat data structures and fields with mixed data types, which can complicate analysis
• Accidental overwriting of validation or formatting when pasting data.
• The inability to fully protect dynamic sheets (such as the “All Departments” tab), although these can be hidden as a workaround.
• The need to manually update lookup tables for staff, sites, and species — requiring oversight from someone with moderate Excel skills.
• Potential underutilisation of Excel’s advanced features, such as Pivot Tables, for comprehensive reporting.
• Potential limits to how many years of data can be practically managed within a single workbook.
• Increasing complexity as reporting needs evolve, requiring careful version control and documentation.

Additionally, there are features of commercial IPM platforms — like automated alerts, integrated image capture, or mobile-friendly interfaces — that could be useful if our needs grow further. However, our current system largely overcomes these Excel limitations, and their drawbacks have not outweighed its benefits. Further detail is provided in the accompanying Suppl. materials 1, 2 here. With routine maintenance, ongoing user training, and careful oversight, our Excel-based system remains a reliable, cost-effective solution that supports both operational needs and sector-wide capacity building.

Our approach aligns with the growing body of literature and practice around IPM in cultural heritage institutions, which increasingly emphasises strategic data use, cross-disciplinary collaboration, and practical adaptability. Globally recognised guidelines — such as those from MuseumsPests.net and the Canadian Conservation Institute (Strang and Kigawa 2022) — highlight the importance of consistent monitoring, standardised terminology, and clear communication tools to support decision-making and organisational learning. Our adoption of the POI, use of structured data validation, and emphasis on accessible visual summaries reflect these international best-practice principles.

This emphasis on accessible, visual reporting also aligns with broader trends in IPM research. Baars et al. (2017) note that effective communication of pest data — through intuitive formats like charts, heat maps, and dashboards — significantly increases its utility, particularly for staff without specialised pest management expertise. When data is easier to interpret, it better supports rapid decision-making, promotes shared understanding, and fosters institutional engagement.

At the same time, this work fills a noticeable gap in Aotearoa’s Museum literature by providing a locally grounded case study that shows how global standards can be adapted to suit specific institutional contexts. It demonstrates that data-informed, participatory IPM can be achieved without specialist software, contributing to both operational effectiveness and sector-wide capability building.

The development of our Excel-based pest monitoring tool at Auckland Museum reflects a broader shift in preventive conservation; towards agile, collaborative, and data-driven approaches that prioritise access, usability, and strategic communication. What began as a simple method to collate data from sticky monitors has evolved into a purpose-built system for shaping risk perception, driving institutional changes, and embedding IPM within the day-to-day operations of the museum.

The success of this tool lies not only in its functionality but in its ability to socialise IPM, that is, to make pest management visible, shared, and relevant across teams. By presenting information in accessible formats and fostering a sense of ownership, we have enhanced data literacy and encouraged broader participation in Collection Care strategies.

Our approach reflects emerging best practices in IPM internationally, not only in terms of data collection, but in how the data is represented and used. Institutions around the world are increasingly adopting communication strategies that translate monitoring data into clear, compelling narratives to support cross-departmental collaboration and organisational learning (Baars et al. 2017). Our spreadsheet mirrors these strategies through its integration of standardised metrics, visual summaries, and emphasis on usability.

Looking ahead, we see opportunities to further build on this foundation. Drawing inspiration from international trends in the communication of pest and environmental data, such as dashboards, real-time alerts, and infographics, we are exploring new ways to visualise, interpret, and share data. This includes:

• Piloting new communication formats such as quarterly IPM updates or briefings embedded in staff updates, to make pest data more immediate and actionable.
• Using the POI figures to develop engaging visual tools, including posters and infographics, to increase awareness and serve as visible reminders to staff.
• Investigating ways to integrate environmental monitoring data (e.g., temperature and relative humidity) to support comparative analysis alongside pest activity trends.

By contributing local insights and practical tools, we also aim to support the development of a more connected and collaborative IPM network within Aotearoa’s GLAM community. Sharing adaptable, low-barrier approaches helps build collective capability and invites others to reflect on how pest data is communicated, interpreted, and acted on in their own contexts.

Globally, climate instability has the potential to drive an increase in pest activity. At the same time, resources will continue to be constrained, making operational efficiency and data-based decision making even more valuable. Increasing reliance on data-informed practice and tools like our monitoring spreadsheet, represent a broader cultural shift — a change in how pest management is perceived, communicated, and embedded across museum teams. Where IPM was once seen as a specialist concern or a tick-box exercise, it is now becoming a vital, visible, and evolving part of everyday museum practice. At Auckland Museum, this shift reflects a broader recognition that while conservators play a central role, effective pest management relies on cross-team collaboration embedded in how we care for collections together.