Hunting bag statistics of wild mammals in Portugal (1989–2022): on the need to improve data report and compilation

Abstract Wildlife monitoring is key for the effective management of natural resources. Hunting-based data (e.g., hunting bag statistics) is an important source of information to overcome the financial constraints imposed to long-term monitoring. Here, taking advantage of three decades (1989–2022) of hunting bag statistics of wild mammals in Portugal, we present the trends and discuss the issues regarding nine game species (five wild ungulates, two carnivores and two lagomorphs). Our results show that hunting bag statistics mirror the general population trends of wild mammal populations at a national scale. The data reflects the increase in number and distribution of roe deer, red deer and wild boar, and the recent expansion experienced by the Egyptian mongoose. The decline in the numbers of European wild rabbit and Iberian hare harvested are also noticeable. Hunting-based data have been used in several research contexts; however, this information is usually affected by geographical, temporal and recording bias. Portugal is no exception. Engaging the interested stakeholders is pivotal to ensure data quality. By reporting reliable and systematically collected hunting data, game managers and wildlife researchers will be able to better understand the main drivers of population dynamics, adjust hunting bags towards sustainable yield and assess the ecological and socio-economic relevance of the hunting sector.

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Publication Details

Journal
European Journal of Wildlife Research
Published
2024-09-16
DOI
https://doi.org/10.1007/s10344-024-01850-y
Citations
8
Primary Topic
Wildlife Ecology and Conservation
Type
article
Field-Weighted Citation Impact
3.67

Funders

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article

Hunting bag statistics of wild mammals in Portugal (1989–2022): on the need to improve data report and compilation

Carlos Fonseca, Daniela Teixeira, Rita Tinoco Torres, João Carvalho et al.
8 citations
European Journal of Wildlife Research
Wildlife Ecology and Conservation
3.67
article

Hunting bag statistics of wild mammals in Portugal (1989–2022): on the need to improve data report and compilation

Carlos Fonseca, Daniela Teixeira, Rita Tinoco Torres, João Carvalho, Dário Hipólito
article en
8 citations

Abstract

Abstract Wildlife monitoring is key for the effective management of natural resources. Hunting-based data (e.g., hunting bag statistics) is an important source of information to overcome the financial constraints imposed to long-term monitoring. Here, taking advantage of three decades (1989–2022) of hunting bag statistics of wild mammals in Portugal, we present the trends and discuss the issues regarding nine game species (five wild ungulates, two carnivores and two lagomorphs). Our results show that hunting bag statistics mirror the general population trends of wild mammal populations at a national scale. The data reflects the increase in number and distribution of roe deer, red deer and wild boar, and the recent expansion experienced by the Egyptian mongoose. The decline in the numbers of European wild rabbit and Iberian hare harvested are also noticeable. Hunting-based data have been used in several research contexts; however, this information is usually affected by geographical, temporal and recording bias. Portugal is no exception. Engaging the interested stakeholders is pivotal to ensure data quality. By reporting reliable and systematically collected hunting data, game managers and wildlife researchers will be able to better understand the main drivers of population dynamics, adjust hunting bags towards sustainable yield and assess the ecological and socio-economic relevance of the hunting sector.

European Journal of Wildlife ResearchVol. 70(5)
Forest Research (GB), University of Aveiro (PT)
Universidade de Aveiro, Ministério da Ciência, Tecnologia e Ensino Superior, Centro de Estudos Ambientais e Marinhos, Universidade de Aveiro, Fundação para a Ciência e a Tecnologia
Openalex Percentile: Top 7%
Wildlife Ecology and Conservation
3.67
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