Data driven fuzzy suitability modelling as a method for assessing habitat choice of migratory Red Kites (Milvus, milvus) across Spain
(2019) In Student thesis series INES NGEM01 20171Dept of Physical Geography and Ecosystem Science
- Abstract
- As an increasing number of species drift onto the IUCN’s list of endangered species, it has become increasingly important to understand the fundamental aspects that are essential in maintaining the fitness of these threatened species.
Using tracking data from 75% of a sample of Swiss juvenile Red Kites during their wintering period in Spain, various bioclimatic predictors (BCMPs) were taken into account to create fuzzy membership functions based on the spatial distribution of the related biogeographic elements within the operating range of the sample set of kites. The aim was to produce a continuous surface of values between 0 and 1, 1 being most suitable and 0 unsuitable for Red Kites across Spain.
Using these functions, weighted... (More) - As an increasing number of species drift onto the IUCN’s list of endangered species, it has become increasingly important to understand the fundamental aspects that are essential in maintaining the fitness of these threatened species.
Using tracking data from 75% of a sample of Swiss juvenile Red Kites during their wintering period in Spain, various bioclimatic predictors (BCMPs) were taken into account to create fuzzy membership functions based on the spatial distribution of the related biogeographic elements within the operating range of the sample set of kites. The aim was to produce a continuous surface of values between 0 and 1, 1 being most suitable and 0 unsuitable for Red Kites across Spain.
Using these functions, weighted linear combinations were undertaken to derive the best combination of BCMPs and what weight each carried that would best explain the distribution of the sample set. This was then validated against the remaining 25% to assess how well the most performant WLC would explain their distribution.
Although the training data sample managed to explain its own distribution with some success, it failed to explain the distribution of the validation set with any statistical success. This could be partially attributed to the absence of potentially significant predictors, such as a food source, which for the red kite, mostly consists of small mammals. This, along with the generally elevated species plasticity and sample size, could have been responsible for the shortcomings in the model’s predictive abilities.
er migration to Spain. During the months of December 2016 and January 2017 the data received was analysed and fuzzy membership functions were created based on 75% of the data, the training set, using different bioclimatic predictors that were deemed potentially important to species habitat choice. These functions were then combined using various weighted linear combinations (WLC), with both the combinations of predictors and their respective weights being altered in to best explain the habitat choice of the training set, the best result then being validated against the remaining 25% of the data, the validation set. Although the training set was well explained by the resulting WLC’s, the model failed to explain the validation set’s habitat choice with any statistical success. The model showed that no single predictor had the ability to explain the habitat choice and could be partially attributed to the absence of potentially significant predictors, such as a food source, which for the red kite, mostly consists of small mammals. This, along with the generally elevated species plasticity, could have been responsible for the shortcomings in the model’s predictive abilities. (Less) - Popular Abstract
- Due to the growing number of species threatened by our ever-changing landscape it is more and more important to understand why some animals live where they do. A model was created using data provided by Red Kites to highlight areas of conservational importance.
Using tracking information from young kites migrating to Spain over the winter period, information on the surrounding biological and geographical elements was measured to discern any patterns which would enable a better understanding on where they choose to spend their time. Using these patterns, a model was created to assign values to the whole of Spain that show how suitable each area was. The scale of the values ranged from 0, areas highly unsuitable, to 1, areas considered very... (More) - Due to the growing number of species threatened by our ever-changing landscape it is more and more important to understand why some animals live where they do. A model was created using data provided by Red Kites to highlight areas of conservational importance.
Using tracking information from young kites migrating to Spain over the winter period, information on the surrounding biological and geographical elements was measured to discern any patterns which would enable a better understanding on where they choose to spend their time. Using these patterns, a model was created to assign values to the whole of Spain that show how suitable each area was. The scale of the values ranged from 0, areas highly unsuitable, to 1, areas considered very suitable for Red Kites
Following the creation of the model it was validated against existing data on Red Kite distribution. Although some there were some discernible patterns when looking at that locations of the young kites, this did not translate with great success when validating the model’s predictions against the distribution of the current Red Kite populations. Following this observation, conclusions were drawn that the original sample of kites was too small along with the fact that Red Kites are simply too opportunistic and adaptable. Further analysis would be required to isolate the exact biological and geographical elements that dictate their habitat choice. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8974440
- author
- Monks, John-William LU
- supervisor
- organization
- course
- NGEM01 20171
- year
- 2019
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Physical Geography and Ecosystems analysis, Fuzzy suitability, GIS, Red Kite, Suitability modelling, Conservation, Habitat choice
- publication/series
- Student thesis series INES
- report number
- 472
- language
- English
- id
- 8974440
- date added to LUP
- 2019-04-30 08:53:45
- date last changed
- 2019-04-30 08:53:45
@misc{8974440,
abstract = {{As an increasing number of species drift onto the IUCN’s list of endangered species, it has become increasingly important to understand the fundamental aspects that are essential in maintaining the fitness of these threatened species.
Using tracking data from 75% of a sample of Swiss juvenile Red Kites during their wintering period in Spain, various bioclimatic predictors (BCMPs) were taken into account to create fuzzy membership functions based on the spatial distribution of the related biogeographic elements within the operating range of the sample set of kites. The aim was to produce a continuous surface of values between 0 and 1, 1 being most suitable and 0 unsuitable for Red Kites across Spain.
Using these functions, weighted linear combinations were undertaken to derive the best combination of BCMPs and what weight each carried that would best explain the distribution of the sample set. This was then validated against the remaining 25% to assess how well the most performant WLC would explain their distribution.
Although the training data sample managed to explain its own distribution with some success, it failed to explain the distribution of the validation set with any statistical success. This could be partially attributed to the absence of potentially significant predictors, such as a food source, which for the red kite, mostly consists of small mammals. This, along with the generally elevated species plasticity and sample size, could have been responsible for the shortcomings in the model’s predictive abilities.
er migration to Spain. During the months of December 2016 and January 2017 the data received was analysed and fuzzy membership functions were created based on 75% of the data, the training set, using different bioclimatic predictors that were deemed potentially important to species habitat choice. These functions were then combined using various weighted linear combinations (WLC), with both the combinations of predictors and their respective weights being altered in to best explain the habitat choice of the training set, the best result then being validated against the remaining 25% of the data, the validation set. Although the training set was well explained by the resulting WLC’s, the model failed to explain the validation set’s habitat choice with any statistical success. The model showed that no single predictor had the ability to explain the habitat choice and could be partially attributed to the absence of potentially significant predictors, such as a food source, which for the red kite, mostly consists of small mammals. This, along with the generally elevated species plasticity, could have been responsible for the shortcomings in the model’s predictive abilities.}},
author = {{Monks, John-William}},
language = {{eng}},
note = {{Student Paper}},
series = {{Student thesis series INES}},
title = {{Data driven fuzzy suitability modelling as a method for assessing habitat choice of migratory Red Kites (Milvus, milvus) across Spain}},
year = {{2019}},
}