Back

Combining participatory mapping and oral histories of veteran fishers to identify long-term environmental change in a nationally significant river

Orchard, S.; Campbell, O.

2025-12-26 ecology
10.64898/2025.12.26.695674 bioRxiv
Show abstract

The detection and monitoring of environmental change is vital for sustainable river management, but historical data are often limited. This study leveraged volunteered geographic information and local ecological knowledge from experienced recreational anglers to identify historical baselines and long-term change in the Rakaia River, a protected catchment under a National Water Conservation Order (WCO) in Aotearoa New Zealand. Oral histories spanning seven decades were recorded using semi-structured interviews with 30 veteran fishers representing 1510 years of combined catchment-specific experience. Inductive thematic analysis and participatory mapping were used to identify physical environment changes and socio-cultural effects associated with recent declines in freshwater fish populations. River environment changes include the loss of natural features such as springs and pools, declines in characteristic native species and habitats, shrinking river mouth la-goon and river plume extents, and interactions with public access. Socio-cultural effects include reduced participation in fishing, community despondence, emotional distress and demographic shifts in fishing hut settlements. Over 20 reported changes involve adverse effects on values that are specifically protected under the WCO, indicative of a policy failure. Declines in multiple indicators of environment health suggest an implementation gap that requires greater attention to environmental monitoring and outcome evaluation yet is hampered by uncertainties around institutional responsibilities. Oral history approaches can help to establish historical baselines for gauging long-term change and enabling adaptive management in this and other data-poor situations.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.