DOI: 10.5061/dryad.7pvmcvf6k
title: Long-term perceptions of freshwater anglers about abandoned, lost or discarded fishing gear during their fishing careers

description: Abandoned, lost, or discarded fishing gear (ALDFG) represents a
 significant pollutant in wetland ecosystems globally. While impacts of
 ALDFG are increasingly recognized as exacting a substantial toll on the
 world’s oceans, reliable data on freshwater ecosystems remain scarce. We
 explored the scale and causes of fishing gear loss in freshwater locations
 by engaging recreational anglers through an online questionnaire in
 Hungary. Respondents provided self-reported estimates of lost fishing gear
 over their angling careers and shared perceptions of the circumstances
 leading to these losses. From the total of 416 responses, 332 respondents
 provided data that we were able to analyse in detail. Results indicated
 that anglers who fished regularly reported greater fishing gear loss than
 older anglers and those who fished predominantly faster flowing waters.
 Using the self-reported numbers, we estimated that at least 126.0–196.2
 million fishing gear items have accumulated in Hungary's freshwater
 over the past 18 years. Annual lost fishing gear items, therefore, must
 weigh 1,800–2,900 tons, equivalent to ~40,500 tons (40.5 million kg) over
 this study period. If current trends persist and based upon time-series
 modeling, fishing gear loss could amount to 301.0–468.7 million items in
 the next 25 years. Our findings highlight the urgent need for targeted
 interventions, such as public awareness campaigns and gear recovery
 programs, to mitigate this growing environmental pollution problem.
description: # Long-term perceptions of freshwater anglers about abandoned, lost or
 discarded fishing gear during their fishing careers Dataset DOI:
 [10.5061/dryad.7pvmcvf6k](https://doi.org/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](https://energofish.hu)) * Halcatraz
 ([https://www.halcatraz.hu](https://www.halcatraz.hu)) * Eurostar
 ([https://www.eurostar.hu](https://www.eurostar.hu)) * Xfish
 ([https://www.xfish.hu](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.