Geostatistical techniques for predicting bird species occurrences
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Habitat loss and fragmentation are major threats to biodiversity. Geostatistical methods, especially kriging, are widely used in ecology. Bird counts data often fail to show normal distribution over an area which is required for most of the kriging methods. Hence choosing an interpolation method without understanding the implications may lead to bias results. United Kingdom’s Exprodat Consulting Ltd had set an Exploratory Spatial Data Analysis (ESDA) workflow for optimising interpolation of petroleum dataset. This workflow was applied in this study to predict capercaillie bird species over whole Sweden. There was no trend found in the dataset. Also the dataset was not spatially auto-correlated. A completely regularized spline surface model was created with RMSE 1.336. Medium to high occurrences (8-16) were found over two very small areas, within Västerbottens county and Västra Götlands county. Low occurrences (1-3) were found all over Sweden. Urban areas like Stockholm city and Malmö city had low occurrences. Another kriging prediction surface was created with RMSE 1.314 to compare the results. There were no prediction values from 5 to 16 in kriging surface. In-depth studies were carried out by selecting three areas. The studies showed that the results of local kriging surfaces did not match with the results of global surface. Uncertainty in GIS may exist at any level. Having low RMSE value does not always mean a good result. Hence ESDA before choosing interpolation method is an effective way. And a post result field investigation could make it more valid.
Regression analysis is also widely used in ecology and there are certain different methods that are available to be used. Ordinary Least Squares is the first method that was tested upon bird counts data set. Adjusted R-squared value was 0.008616 which indicated that explanatory variables pine, spruce, roads, urban areas and wetlands were just contributing to 0.8% to the dependent variable bird counts. It was also found that there was no linear relationship between dependent and explanatory variables. Logistic regression was the next step as it had the capability to work with nonlinear data also. The Spatial Data Modeller (SDM) tool was used to perform logistic regression in ArcGIS 9.3. Initially results of logistic regression were unexpected, hence focal statistics was performed upon all the independent variables. Logistic regression with these new independent variables generated meaningful results. This time the probability of occurrence of birds had weak positive relationship with all the independent variables. Coefficients of pine, spruce, roads, urban areas and wetlands were found to be 0.39, 0.23, 0.13, 0.24 and 0.14 respectively. Pine and spruce are natural attractors for birds, hence results were quite acceptable. But the overall model performance remained poor. Positive coefficient for roads, urban areas and wetlands may well be due to redundancy in these datasets or observer bias in bird species reporting. IDRISI Andes also came up with almost the same results when logistic regression with same dependent and independent variables was performed. IDRISI Andes output contained the pseudo R-square value, found to be 0.0416. This was an indication of biasness in the dataset also. The results of in-depth studies by selecting three areas also showed that LR with focal statistics were having better results than LR without focal statistics, but the overall performance remained poor. The SDM tool is a good choice for performing logistic regression on small scale datasets due to its limitation. Comparison of results between the two geostatistical methods, interpolation and regression depicts the similarity at discrete places; an unbiased dataset might have resulted in a better comparison of two methods.
Place, publisher, year, edition, pages
2011. , 87 p.
, TRITA-GIT, ISSN 1653-5227 ; EX 11-012
ESDA, Kriging, Spline, OLS, SDM, Logistic regression
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-68308ISRN: ISRN KTH/GIT/EX--11/012-SEOAI: oai:DiVA.org:kth-68308DiVA: diva2:485136
Subject / course
Degree of Master - Geodesy and Geoinformatics
Mörtberg, Dr. Ulla, Assistant Professor
Ban, Yifang, Professor