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2. Background on Species Distribution Modelling

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2. Background on Species Distribution Modelling A new tool for predicting distribution patterns of African dragonflies in space and time: sensitivity analyses of model parameters and environmental variables A thesis submitted in partial fulfilment of the requirements of the degree of Doctor of Natural Sciences (Dr. rer. nat.) of the Faculty of Environment and Natural Resources, Albert-Ludwigs-Universität Freiburg im Breisgau, Germany Submitted by Nirmal Ojha from Nepal Freiburg im Breisgau 2014 Dean: Prof. Dr. Barbara Koch Supervisor: Prof. Dr. Axel Drescher 2nd Reviewer : Prof. Dr. Carsten F. Dormann Date of thesis' defence : 19 Nov 2013 Acknowledgements Firstly I would like to thank the Faculty of Environment and Natural Resources for accepting my application for pursuing a doctoral degree. Many thanks go to Prof Dr Axel Drescher for supervising my research work. My warm thanks to Prof Dr Carsten F Dormann for accepting to review my work. Special thanks are due to Prof Dr Gertrud Schaab from the Hochschule Karlsruhe who supervised my work offering her guidance throughout the entire period of my work. I am grateful to the office of the Chancellor of the Hochschule Karlsruhe and helpful team of the Institute of Applied Research (IAF) for providing me the work space and allowing me to use the facilities. Acknowledgement is also due to Christian Stern who organised each year the necessary ArcGIS developer’s license in timely manner. I am thankful to Dr Viola Clausnitzer, Dr Frank Suhling, Dr K. D-B Dijkstra and Jens Kipping for providing me the records of dragonflies’ locations. The discussions and feedbacks offered by them on the initial predicted distributions of some species contributed in improving the modelling tool, thereby better prediction results. My appreciations are also to the former members of the working group G(V)ISЯ at the IAF while being part of the BIOTA Africa project for their friendship, moral support and cordial working environment making part of the journey enjoyable. Thanks also to Dorothea Heim who helped translating the extended summary in German. I am indebted to Prof Dr Prajwal Lal Pradhan from the Institute of Engineering, Pulchowk Campus Nepal. He has been motivational figure and his advice and moral support has led me to this position today. Finally, I would like to express sincere gratitude to my parents for their support. Page| iii Page| iv Abstract In the last few decades, Africa has been a dynamic continent regarding the changes in landscape, population and climate. To identify effects of the changes in environmental conditions on biodiversity, species distribution modelling (SDM) can be of use and SDM has been used in wide array of ecological applications such as determining hotspots, planning of reserves, designing survey for biodiversity inventory, or assessing the impacts of environmental change on biodiversity. Odonata which require both terrestrial and aquatic ecosystem for a lifecycle, is suitable species to consider as flagship species for many ecological studies. Here, a logistic regression based new SDM tool, the ‘SpeeDi Tool' is presented focusing on modelling the distribution of African Odonata species using the Odonata Database of Africa. The use of geographic information system (GIS) in pre- and post- processing is integral part of the SDM workflow and GIS and statistical modelling is integrated in the SpeeDi Tool. The user centred approach for the development of the SpeeDi Tool offers usability and achievement of the goal (i.e. predicting the distribution range) with ease. Pseudagrion kersteni , a widely spread dragonfly species in sub-Saharan Africa, is taken as species of interest to demonstrate the use and ability of the SpeeDi Tool. An expert-drawn watershed based range map from IUCN serves the purpose for visually comparing the modelled spatial distribution and, thus, enables to evaluate the predicted range. The SpeeDi Tool has several modelling parameters, some of which have been new in SDM field, namely, elastic-net factor which has not been applied to SDM using background samples until now, soft buffer threshold (SBT) which is a new concept introduced here, and weights for samples. In addition to the use of background samples, it introduces the modelling by using presence samples with absence and / or background samples; the combination of presence, absence and background samples is a new option not found in existing SDM tools yet. In order to gain confidence in using the SpeeDi Tool, several sensitivity analyses are performed using P. kersteni samples for different modelling approaches for applying different modelling parameters and for using different environmental geodatasets. These sensitivity analyses are thought for determining the optimum values of different regression parameters to maximise the model’s performance, and for finding the important environmental variables and their effects on the prediction of distribution ranges. The concept similar to that of a virtual species is used to evaluate general applicability of the SpeeDi Tool. The sensitivity analyses of modelling parameters showed a) the elastic-net regularisation is superior to L1 or L2 regularisation, b) the uncertainty in population prevalence in background samples can be reduced by applying SBT, c) weights can be effective in reducing effects of sampling bias, d) the number of background samples is sensitive for fitting the model, and e) product interaction of variables are necessary for better prediction of distribution range. The sensitivity of environmental datasets showed a) monthly climate datasets should be preferred over synthesised bioclimatic datasets, b) predicted distributions using land-cover datasets with different classification schemes are not much different but the contribution of land cover classes in different datasets indicated that false interpretation regarding ecological significance of these classes can be possible. Further, the results for the modelling of A. minuscula showed that there is not much difference in distribution range when modelled at spatial resolutions of 1 km and 8 km. The results also indicated that modelling extent should not extend too far beyond the species’ native region. Page| v Page| vi Table of Contents Acknowledgements ................................................................................................................................ iii Abstract ................................................................................................................................................... v List of Figures ........................................................................................................................................... x List of Tables .......................................................................................................................................... xiii List of Boxes ............................................................................................................................................ xv List of acronyms ...................................................................................................................................... xv 1. Introduction ..................................................................................................................................... 1 Background .............................................................................................................................. 1 The thesis’ aims and limits....................................................................................................... 2 Outline of the thesis ................................................................................................................ 3 2. Background on species distribution modelling ............................................................................... 5 Three types of species distribution models ............................................................................. 5 Uses of species distribution modelling .................................................................................... 6 Empirical methods for species distribution modelling ............................................................ 8 Use of presence, absence and background data in species distribution modelling ............. 10 General characteristics and assumptions of statistical species distribution models ............ 12 Commonly used data in statistical species distribution models ........................................... 15 Incorporating statistics and GIS for species distribution modelling ...................................... 16 Summary and main considerations ....................................................................................... 18 3. Quality-in-use for development of species distribution modelling tool ....................................... 20 Context of use ........................................................................................................................ 20 Quality measures ................................................................................................................... 21 3.2.1. Functionality .................................................................................................................. 22 3.2.2. Reliability ....................................................................................................................... 22 3.2.3. Usability ......................................................................................................................... 23 3.2.4.
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