Vše

Co hledáte?

Vše
Projekty
Výsledky výzkumu
Subjekty

Rychlé hledání

  • Projekty podpořené TA ČR
  • Významné projekty
  • Projekty s nejvyšší státní podporou
  • Aktuálně běžící projekty

Chytré vyhledávání

  • Takto najdu konkrétní +slovo
  • Takto z výsledků -slovo zcela vynechám
  • “Takto můžu najít celou frázi”

Validation and inference of high-resolution information (downscaling) of ENETwild abundance model for wild boar

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F20%3A81652" target="_blank" >RIV/60460709:41320/20:81652 - isvavai.cz</a>

  • Výsledek na webu

    <a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2020.EN-1787" target="_blank" >https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2020.EN-1787</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.2903/sp.efsa.2020.EN-1787" target="_blank" >10.2903/sp.efsa.2020.EN-1787</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Validation and inference of high-resolution information (downscaling) of ENETwild abundance model for wild boar

  • Popis výsledku v původním jazyce

    The ENETWILD consortium provided in August 2019 a map at 10x10 km resolution for wild boar abundance based on hunting data. The availability of prediction maps at a spatial resolution comparable with the one of the home range of wild boar can be useful for further evaluation of risk of spread of African swine fever (ASF). Therefore, predictions of abundance on the basis of the wild boar home range are required. The downscaling procedure needs information on what resolution level is being used for predictions (hunting grounds, municipalities and NUTS3). This report presents the validation of previously produced hunting yield maps (10x10 km resolution) and new model projections downscaled at 2x2 km resolution. A new dataset based on hunting bag numbers was used as external data for validation. These data were arranged at two levels: at country level for the European scenario and at NUTS3 level for a scenario in Spain, where the data availability is higher than the rest of Europe in terms of quantity and quality. Very similar geographical patterns of wild boar abundance were obtained when the models were transferred to 2x2 km grid. The downscaled model predictions were aggregated at country and NUTS3 levels and compared against the external dataset. Our study confirmed that both 10x10 km and 2x2 km resolutions were able to detect spatial variation in wild boar hunting bags (high model performance) and to predict the numbers of wild boar hunted with relative precision (moderate model accuracy). Nevertheless, an overestimation of absolute number of hunted wild boar was observed using both resolutions. Reasons for this overestimation are discussed in this report. The linearity between predictions of hunting yield and external dataset was maintained, indicating that hunting yield predictions can be considered as a good proxy of wild boar abundance. Therefore, updated wild boar hunting yield data, collected at the finest spatial resolution as possible, is needed to correctly recalibrate our model at regional level, an in particular in eastern European countries.

  • Název v anglickém jazyce

    Validation and inference of high-resolution information (downscaling) of ENETwild abundance model for wild boar

  • Popis výsledku anglicky

    The ENETWILD consortium provided in August 2019 a map at 10x10 km resolution for wild boar abundance based on hunting data. The availability of prediction maps at a spatial resolution comparable with the one of the home range of wild boar can be useful for further evaluation of risk of spread of African swine fever (ASF). Therefore, predictions of abundance on the basis of the wild boar home range are required. The downscaling procedure needs information on what resolution level is being used for predictions (hunting grounds, municipalities and NUTS3). This report presents the validation of previously produced hunting yield maps (10x10 km resolution) and new model projections downscaled at 2x2 km resolution. A new dataset based on hunting bag numbers was used as external data for validation. These data were arranged at two levels: at country level for the European scenario and at NUTS3 level for a scenario in Spain, where the data availability is higher than the rest of Europe in terms of quantity and quality. Very similar geographical patterns of wild boar abundance were obtained when the models were transferred to 2x2 km grid. The downscaled model predictions were aggregated at country and NUTS3 levels and compared against the external dataset. Our study confirmed that both 10x10 km and 2x2 km resolutions were able to detect spatial variation in wild boar hunting bags (high model performance) and to predict the numbers of wild boar hunted with relative precision (moderate model accuracy). Nevertheless, an overestimation of absolute number of hunted wild boar was observed using both resolutions. Reasons for this overestimation are discussed in this report. The linearity between predictions of hunting yield and external dataset was maintained, indicating that hunting yield predictions can be considered as a good proxy of wild boar abundance. Therefore, updated wild boar hunting yield data, collected at the finest spatial resolution as possible, is needed to correctly recalibrate our model at regional level, an in particular in eastern European countries.

Klasifikace

  • Druh

    V<sub>souhrn</sub> - Souhrnná výzkumná zpráva

  • CEP obor

  • OECD FORD obor

    40301 - Veterinary science

Návaznosti výsledku

  • Projekt

  • Návaznosti

    S - Specificky vyzkum na vysokych skolach

Ostatní

  • Rok uplatnění

    2020

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Počet stran výsledku

    23

  • Místo vydání

  • Název nakladatele resp. objednatele

  • Verze