---
title: "Participatory monitoring"
chunk: 2/3
source: "https://en.wikipedia.org/wiki/Participatory_monitoring"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T04:15:26.931405+00:00"
instance: "kb-cron"
---

For connecting knowledge systems: in efforts to bring Indigenous and local knowledge systems into the science–policy interface such as the Intergovernmental Platform for Biodiversity and Ecosystem Services.
For monitoring rapidly changing environments: to inform resource management in rapidly changing environments such as the Arctic, where Indigenous and local communities have detailed knowledge of key components of their environment, such as sea-ice, snow, weather patterns, caribou and other natural resources.
In Payment for Ecosystem Services (PES) programs: to connect environmental performance with payment schemes such as REDD+.
For reinforcing international agreements: in efforts to link international environmental agreements to decision-making in the ‘real world’.

=== Typology ===
A typology of monitoring schemes has been proposed, determined on the basis of relative contributions of local stakeholders and professional researchers. and supported by findings from statistical analysis of published schemes. The typology identified 5 categories of monitoring schemes that between them span the full spectrum of natural resource monitoring protocols:
Category A. Autonomous Local Monitoring. In this category the whole monitoring process—from design, to data collection, to analysis, and finally to use of data for management decisions—is carried out autonomously by local stakeholders. There is no direct involvement of external agencies. For an example see.
Category B. Collaborative Monitoring with Local Data Interpretation. In these schemes, the original initiative was taken by scientists but local stakeholders collect, process and interpret the data, although external scientists may provide advice and training. The original data collected by local people remain in the area being monitored, which helps create local ownership of the scheme and its results, but copies of the data may be sent to professional researchers for in-depth or larger-scale analysis. Examples are included in.
Category C. Collaborative Monitoring with External Data Interpretation. The third most distinct group is monitoring scheme category C. These schemes were designed by scientists who also analyse the data, but the local stakeholders collect the data, take decisions on the basis of the findings and carry out the management interventions emanating from the monitoring scheme. Examples are provided in.
Category D. Externally Driven Monitoring with Local Data Collectors. This category of monitoring scheme involves local stakeholders only in data collection. The design, analysis, and interpretation of the monitoring results are undertaken by professional researchers—generally far from the site. Monitoring schemes of category D are mostly long-running ‘citizen science’ projects from Europe and North America. See for example
Category E. Externally Driven, Professionally Executed Monitoring. Monitoring schemes of category E do not involve local stakeholders. Design of the scheme, analysis of the results, and management decisions derived from these analyses are all undertaken by professional scientists funded by external agencies. An example is

== The use of technology for participatory monitoring ==
Traditional methods of data collection for participatory monitoring use paper and pen. This has advantages in terms of low cost of materials and training, simplicity, and reduced potential for technical hitches. However, all data must be transcribed for analysis, which takes time and can be subject to transcription errors. Increasingly, participatory monitoring initiatives incorporate technology, from GPS recorders to georeference the data collected on paper, to drones to survey remote areas, phones to send simple reports via SMS, or smartphones to collect and store data. Various apps exist to create and manage data collection forms on smartphones (e.g. ODK, Sapelli and others).
Some initiatives find that the use of smartphones for data collection has advantages over paper-based systems.  The advantages include that very little equipment need be carried on a survey, a large amount and variety of data can be stored (geographical locations, photos and audio, as well as data entered onto monitoring forms) and data can be shared rapidly for analysis without transcription errors. The use of smartphones can incentivise young people to get involved in monitoring, sparking an interest in conservation. Some apps are especially designed to be usable by illiterate monitors. If local people risk threats or violence by monitoring illegal activities, the true purpose of the phones can be denied, and the monitoring data locked away. However, phones are expensive; are vulnerable to damage and technical issues; necessitate additional training - not least due to rapid technological change; phone charging can be a challenge (especially under thick forest canopies); and uploading data for analysis is difficult in areas without network connections.

== Data sharing in participatory monitoring ==
A key challenge for participatory monitoring is to develop ways to store, manage and share data and to do this in ways that respect the rights of the communities that supplied the data. A ‘rights-based approach to data sharing’ can be based on principles of free, prior and informed consent, and prioritise the protection of the rights of those who generated the data, and/or those potentially affected by data-sharing. Local people can do much more than simply collect data: they can also define the ways that this data is used, and who has access to it.
Clear agreements on data sharing are especially important for initiatives where diverse data is collected, of variable relevance to different stakeholders. For example, monitoring could on the one hand, investigate sensitive social problems within a community, or contested resources at the centre of local conflicts or illegal exploitation - data that community leaders might want to keep confidential and address locally; on the other hand, the same initiative could generate data on forest biomass, of greater interest to external stakeholders.
One way to establish the rules around data sharing is to set up a data sharing protocol. This can define: