Statistical Methods for the Joint Analysis of Spatial, Sparse or Missing Ecological Data

To mitigate biodiversity loss, we need to understand the environmental and demographic causes of changes in the distributions and abundances of species. Bird populations are in a continual state of flux; these fluctuations can be explained by changes in vital rates, such as survival and productivity...

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Main Author: Jiménez Muñoz, Marina
Format: Thesis
Language:English
Published: 2020
Subjects:
Online Access:https://kar.kent.ac.uk/79692/
https://kar.kent.ac.uk/79692/1/108JimenezMunoz_Marina_thesis_afterviva.pdf
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spelling ftkentuniv:oai:kar.kent.ac.uk:79692 2023-05-15T13:16:24+02:00 Statistical Methods for the Joint Analysis of Spatial, Sparse or Missing Ecological Data Jiménez Muñoz, Marina 2020-01 application/pdf https://kar.kent.ac.uk/79692/ https://kar.kent.ac.uk/79692/1/108JimenezMunoz_Marina_thesis_afterviva.pdf en eng https://kar.kent.ac.uk/79692/1/108JimenezMunoz_Marina_thesis_afterviva.pdf Jiménez Muñoz, Marina (2020) Statistical Methods for the Joint Analysis of Spatial, Sparse or Missing Ecological Data. Doctor of Philosophy (PhD) thesis, University of Kent,. (KAR id:79692 </79692>) Thesis NonPeerReviewed 2020 ftkentuniv 2023-03-12T19:17:05Z To mitigate biodiversity loss, we need to understand the environmental and demographic causes of changes in the distributions and abundances of species. Bird populations are in a continual state of flux; these fluctuations can be explained by changes in vital rates, such as survival and productivity (breeding success). This thesis is the result of three different ecological projects for which we have developed statistical methods that combine different types of data together. In particular, in this thesis we describe, implement, and develop statistical models that can be applied to different types of ecological data such as census, ring-recovery, and capture-recapture data. In the first project we use data from the British Trust for Ornithology (BTO), which has developed an extensive historical data set of the total number of birds ringed in Britain and Ireland, dating back to 1909. However, until 2000 the data were submitted by ringers in paper form. The way in which such archival data were collected and stored means that the total number of birds ringed in different age categories is difficult to obtain. Bird survival changes with age, with younger birds being more vulnerable. Missing information on the age at ringing compromises our ability to understand historic variation in survival rates. We examine suitable methods and propose a new model for enhancing the use of such data. Using blackbird (Turdus merula) and sandwich tern (Thalasseus sandvicensis) data we show the rigour of our model in estimating unknown age proportions for different species, allowing the BTO and other European institutions to fully utilise their historical data. Bi-parental care is crucial in the reproduction success of the little auk (Alle alle) which we study in the second project. For this species, typically, the female deserts the brood before the male does. Hypotheses which considered that females left the nest earlier in order to increase their remating success or to secure their good body conditions have been rejected. As a ... Thesis Alle alle little auk University of Kent: KAR - Kent Academic Repository
institution Open Polar
collection University of Kent: KAR - Kent Academic Repository
op_collection_id ftkentuniv
language English
description To mitigate biodiversity loss, we need to understand the environmental and demographic causes of changes in the distributions and abundances of species. Bird populations are in a continual state of flux; these fluctuations can be explained by changes in vital rates, such as survival and productivity (breeding success). This thesis is the result of three different ecological projects for which we have developed statistical methods that combine different types of data together. In particular, in this thesis we describe, implement, and develop statistical models that can be applied to different types of ecological data such as census, ring-recovery, and capture-recapture data. In the first project we use data from the British Trust for Ornithology (BTO), which has developed an extensive historical data set of the total number of birds ringed in Britain and Ireland, dating back to 1909. However, until 2000 the data were submitted by ringers in paper form. The way in which such archival data were collected and stored means that the total number of birds ringed in different age categories is difficult to obtain. Bird survival changes with age, with younger birds being more vulnerable. Missing information on the age at ringing compromises our ability to understand historic variation in survival rates. We examine suitable methods and propose a new model for enhancing the use of such data. Using blackbird (Turdus merula) and sandwich tern (Thalasseus sandvicensis) data we show the rigour of our model in estimating unknown age proportions for different species, allowing the BTO and other European institutions to fully utilise their historical data. Bi-parental care is crucial in the reproduction success of the little auk (Alle alle) which we study in the second project. For this species, typically, the female deserts the brood before the male does. Hypotheses which considered that females left the nest earlier in order to increase their remating success or to secure their good body conditions have been rejected. As a ...
format Thesis
author Jiménez Muñoz, Marina
spellingShingle Jiménez Muñoz, Marina
Statistical Methods for the Joint Analysis of Spatial, Sparse or Missing Ecological Data
author_facet Jiménez Muñoz, Marina
author_sort Jiménez Muñoz, Marina
title Statistical Methods for the Joint Analysis of Spatial, Sparse or Missing Ecological Data
title_short Statistical Methods for the Joint Analysis of Spatial, Sparse or Missing Ecological Data
title_full Statistical Methods for the Joint Analysis of Spatial, Sparse or Missing Ecological Data
title_fullStr Statistical Methods for the Joint Analysis of Spatial, Sparse or Missing Ecological Data
title_full_unstemmed Statistical Methods for the Joint Analysis of Spatial, Sparse or Missing Ecological Data
title_sort statistical methods for the joint analysis of spatial, sparse or missing ecological data
publishDate 2020
url https://kar.kent.ac.uk/79692/
https://kar.kent.ac.uk/79692/1/108JimenezMunoz_Marina_thesis_afterviva.pdf
genre Alle alle
little auk
genre_facet Alle alle
little auk
op_relation https://kar.kent.ac.uk/79692/1/108JimenezMunoz_Marina_thesis_afterviva.pdf
Jiménez Muñoz, Marina (2020) Statistical Methods for the Joint Analysis of Spatial, Sparse or Missing Ecological Data. Doctor of Philosophy (PhD) thesis, University of Kent,. (KAR id:79692 </79692>)
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