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The hunt for the elusive Cantharellales: How to stake the odds in the mycophiles favour, using Species Distribution Modelling and GIS

Frank-Hansen, Maiken LU (2024) In Student thesis series INES NGEK01 20241
Dept of Physical Geography and Ecosystem Science
Abstract
For centuries, people have appreciated the taste of wild mushrooms which can be found in nature throughout the European continent and the Fennoscandian peninsula. This is especially so for those of the order of Cantharellales, where you find the Golden Chanterelle among others. Today the commercial aspect of mushroom harvesting has resulted in regulations and guidelines set out by the Nordic Council of Ministers.
With the help of Geographic Information Systems (GIS) and Species Distribution Models (SDM) it is now possible to ascertain a probability of occurrence that helps guide the mushroom pickers to the best spots in a designated area. This thesis aims to map and evaluate how accurately the SDM model, Maxent, will predict the locations... (More)
For centuries, people have appreciated the taste of wild mushrooms which can be found in nature throughout the European continent and the Fennoscandian peninsula. This is especially so for those of the order of Cantharellales, where you find the Golden Chanterelle among others. Today the commercial aspect of mushroom harvesting has resulted in regulations and guidelines set out by the Nordic Council of Ministers.
With the help of Geographic Information Systems (GIS) and Species Distribution Models (SDM) it is now possible to ascertain a probability of occurrence that helps guide the mushroom pickers to the best spots in a designated area. This thesis aims to map and evaluate how accurately the SDM model, Maxent, will predict the locations of five specified Cantharellales species. By combining six different environmental factors with collected sample points from the 2023 mushroom season for Svedala Municipality and external sample points, collected over a 20-year time period from GBIF and Inaturalist, a model was developed. Both are online international networks that provide open access to species data for scientist and researcher as well as land managers and the public. The model produced a good fit for Svedala Municipality and a fair fit for Scania County. The evaluation was based on Areas Under the Curve values (AUC) derived from the Receiver Operating Characteristic curve (ROC). The curves values range from 0 to 1 and the results from the respective models were a mean AUC of 0.762 for Scania County and a 0.875 for Svedala Municipality.
Of the six environmental factors, the land cover layer was expected to have the highest influence establishing the model and within the model. This was also confirmed, however, the National Land Cover Data of 2018 clearly outperformed that of the more generalised land cover of the CORINE land cover classification. Additionally, specific local characteristics of an area were deemed as rather significant, in terms of its influence and contribution of data to the model.
Overall, the model indicated that it was possible to achieve a valid prediction of Cantharellales mushroom occurrence using open-source datasets and SDM modelling. With the development of future web applications that help landowners track the effects their management techniques have on the fungi habitats; the growth of this specific ecosystem service will benefit both the landowners and the mycophiles. (Less)
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author
Frank-Hansen, Maiken LU
supervisor
organization
course
NGEK01 20241
year
type
M2 - Bachelor Degree
subject
keywords
Geographic Information Systems, GIS, Modelling, Species Distribution Model, SDM, Maxent, Mushrooms, Fungi, Cantharellales, The Golden Chanterelle, Cantharellus cibarius, Winter Chanterelle, Craterellus tubaeformis, Yellow Foot, Craterellus lutescens. The Black Trumpet, Craterellus cornucopioides
publication/series
Student thesis series INES
report number
640
language
English
id
9163971
date added to LUP
2024-06-14 16:32:42
date last changed
2024-06-14 16:32:42
@misc{9163971,
  abstract     = {{For centuries, people have appreciated the taste of wild mushrooms which can be found in nature throughout the European continent and the Fennoscandian peninsula. This is especially so for those of the order of Cantharellales, where you find the Golden Chanterelle among others. Today the commercial aspect of mushroom harvesting has resulted in regulations and guidelines set out by the Nordic Council of Ministers.
With the help of Geographic Information Systems (GIS) and Species Distribution Models (SDM) it is now possible to ascertain a probability of occurrence that helps guide the mushroom pickers to the best spots in a designated area. This thesis aims to map and evaluate how accurately the SDM model, Maxent, will predict the locations of five specified Cantharellales species. By combining six different environmental factors with collected sample points from the 2023 mushroom season for Svedala Municipality and external sample points, collected over a 20-year time period from GBIF and Inaturalist, a model was developed. Both are online international networks that provide open access to species data for scientist and researcher as well as land managers and the public. The model produced a good fit for Svedala Municipality and a fair fit for Scania County. The evaluation was based on Areas Under the Curve values (AUC) derived from the Receiver Operating Characteristic curve (ROC). The curves values range from 0 to 1 and the results from the respective models were a mean AUC of 0.762 for Scania County and a 0.875 for Svedala Municipality.
Of the six environmental factors, the land cover layer was expected to have the highest influence establishing the model and within the model. This was also confirmed, however, the National Land Cover Data of 2018 clearly outperformed that of the more generalised land cover of the CORINE land cover classification. Additionally, specific local characteristics of an area were deemed as rather significant, in terms of its influence and contribution of data to the model.
Overall, the model indicated that it was possible to achieve a valid prediction of Cantharellales mushroom occurrence using open-source datasets and SDM modelling. With the development of future web applications that help landowners track the effects their management techniques have on the fungi habitats; the growth of this specific ecosystem service will benefit both the landowners and the mycophiles.}},
  author       = {{Frank-Hansen, Maiken}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Student thesis series INES}},
  title        = {{The hunt for the elusive Cantharellales: How to stake the odds in the mycophiles favour, using Species Distribution Modelling and GIS}},
  year         = {{2024}},
}