Dataset DOI: 10.5061/dryad.7pvmcvf6k

Description of the data and file structure

The dataset originates from an online questionnaire survey conducted among recreational freshwater anglers in Hungary between 2007 and 2024. The aim of the study was to collect information on the loss of abandoned, lost, or otherwise discarded fishing gear (ALDFG) in inland waters.

The survey consisted of 35 questions divided into two sections: (i) socio-demographic characteristics and fishing practices of respondents, and (ii) detailed information on types, quantity, and circumstances of fishing gear losses. In total, 416 anglers participated, of which 332 provided complete responses suitable for statistical analysis.

The dataset includes:

• Respondents’ demographic and socio-economic characteristics (age, gender, occupation, fishing frequency, fishing experience, participation in competitions, boat use, etc.).
• Fishing methods employed and types of waters visited.
• Self-reported numbers and types of fishing gear items purchased and lost during fishing careers.
• Reported circumstances of gear loss and perceptions of environmental impacts.
• Suggestions for preventing gear loss.

The data were used to estimate the total number and weight of lost fishing gear items in Hungarian freshwaters, as well as to forecast future losses using time-series modelling.

Files and variables

File: data_loki_et_al_2025_Ecological_Solutions_and_Evidence.xlsx

Description: his dataset contains responses from an online questionnaire of Hungarian recreational freshwater anglers (2007–2024). It provides demographic information, fishing practices, types and amounts of fishing gear purchased and lost, circumstances of gear loss, and anglers’ perceptions of environmental impacts. A supplementary table includes the annual number of licensed anglers in Hungary. 

Variables

Sheet: data

• gender – Respondent gender.
• fishingfreqscale / fishingfreqcat – Fishing frequency (numeric, categorical).
• fishingyears / fishingyearsnum – Years of fishing experience.
• wtlake, wtriver, … – Water types visited (binary).
• mainwatertype / wtnum – Main water type; number of water types visited.
• competition / boat / consumefish – Fishing practices (yes/no).
• Fishing methods (bottom, float, spinning, catfishing, etc.) – Binary.
• methodmain / methodnum – Main method; number of methods used.
• gearbougth / gearbougthnum – Gear purchased annually.
• jobtype / age / agegroup – Demographics.
• lostgear / gearlost / gearlostnum – Gear loss (category, number).
• lastI–VI…, freqI–VI… – Types of gear last/frequently lost.
• reason / lasttorn / laststolen / … – Circumstances of loss.
• estlostgear – Estimated number of items lost.
• foundlostgear / fishwithhook – Found lost gear; caught fish entangled (yes/no).
• abandonedgearproblem / problemcat / pollutant / dangertowildlife / dangertohuman / obstacle – Perceived problems.
• problemnum – Number of problems identified.

Sheet: nr of anglers per year

• year – Calendar year.
• anglersnum – Licensed anglers (count).

Note on "n/a" Entries

In this dataset, any cell marked as "n/a" indicates that the data is missing because the respondent did not provide an answer to the corresponding question in the questionnaire. These entries do not represent “not applicable” cases, but rather unfilled or omitted responses.

Code/software

The dataset is stored in Microsoft Excel (.xlsx) format and can be opened with any spreadsheet software such as:

• LibreOffice Calc (free, open-source; recommended version ≥7.5)
• Google Sheets (free, browser-based)
• R (v4.2.2 or later) with the following packages:
  • readxl (for reading Excel files)
  • tidyverse (for data wrangling and visualization)
  • forecast (for time-series modelling and Kalman filtering, used in the study)

Workflow used in the study

1. Data input: The raw survey data (data sheet) and the annual number of licensed anglers (nr of anglers per year sheet) were imported into R.
2. Pre-processing: Variables were cleaned, recoded (e.g. binary indicators for fishing methods and water types), and transformed (e.g. log-transformation of skewed variables).
3. Statistical analyses:
   • Linear regression models were fitted to examine relationships between fishing experience, demographics, water type, and gear loss.
   • Time-series analyses (Kalman filter; ARIMA [1,1,0]) were applied to estimate and forecast gear loss at a national level.
4. Visualization: Plots were generated in R (using ggplot2 from the tidyverse package).

No proprietary code or scripts are included with the dataset.

Access information

Other publicly accessible locations of the data:

• None

Data was derived from the following sources:

• The dataset was derived from the following sources:
  1. Online questionnaire survey of Hungarian recreational freshwater anglers, distributed via Facebook groups (2007–2024).
  2. Official statistics on licensed anglers in Hungary (2007–2024), provided by the Hungarian National Anglers’ Association (MOHOSZ) and the National Food Chain Safety Office (NÉBIH).
  3. Secondary data on average fishing gear weights, obtained from Hungarian angling equipment retailers’ websites:

     • Energofish (https://energofish.hu)

     • Halcatraz (https://www.halcatraz.hu)

     • Eurostar (https://www.eurostar.hu)

     • Xfish (https://www.xfish.hu)
       These sources were combined to estimate the number and weight of abandoned, lost, or discarded fishing gear (ALDFG) in Hungarian freshwaters and to forecast future losses.

Human subjects data

The methods of building the online questionnaire obtaining data followed the International Society of Ethnobiology’s Code of Ethics (Ethnobiology I.S.O. 2006). No ethical committee permits were required.