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Ben-Gurion University of the Negev the Jacob Blaustein Institutes for Desert Research the Albert Katz International School for Desert Studies Ben-Gurion University of the Negev The Jacob Blaustein Institutes for Desert Research The Albert Katz International School for Desert Studies Spider diversity and assemblage composition: the known and unknown in pomegranate orchards Thesis submitted in partial fulfillment of the requirements for the degree of "Master of Science" By: Ibrahim N. A. Salman 8th October, 2017 Ben-Gurion University of the Negev The Jacob Blaustein Institutes for Desert Research The Albert Katz International School for Desert Studies Spider diversity and assemblage composition: the known and unknown in pomegranate orchards Thesis submitted in partial fulfillment of the requirements for the degree of "Master of Science" By: Ibrahim N. A. Salman Under the Supervision of Prof. Yael Lubin, Dr. Efrat Gavish-Regev and Prof. David Saltz Department of Desert Ecology Author's Signature … Date 8 Oct. 2017 Approved by the Supervisors… . Date 8 Oct. 2017 Approved by the Director of the School …………… Date 18 Oct. 2017 I Abstract Spiders are considered effective biological control agents in some agroecosystems. Their ability to control pest insects can depend on their diversity, species composition, and abundance, but the factors that determine the spider assemblage in a particular crop are poorly studies. In this research, the effects of climatic gradient and landscape properties on spider diversity, composition and functional groups in pomegranate orchards were investigated by testing predictions based on general ecological hypotheses. The thesis was divided into four chapters that include general introduction (chapter one) and concluding remarks (chapter four). In chapter two, I investigated the effect of the climatic gradient and landscape properties on spider diversity and abundance in pomegranate orchards. In the third chapter, I identified functional groups of spiders collected in the pomegranate orchards and determined important environmental variables that contribute to explaining spider assemblage composition and functional groups. A novel hypothesis, the agricultural landscape evenness hypothesis (ALE), was tested. It predicts that spider diversity will increase with increasing evenness of the areas occupied by different habitats near the orchard. Sampling of spiders was done twice during the pomegranate growing season in 2015 in 12 orchards along the rainfall gradient in Israel. We used two methods: shaking canopy branches combined with visual searching in the trees, and trunk traps placed on the trees and collected after one month. Both methods combined yielded 1804 individuals representing 18 spider families and 37 genera. A 1 km radius around each orchard was used to calculate the proportion of area covered by different habitats in the landscape. Spider diversity showed no pattern with regard to measures of productivity or habitat-heterogeneity, but was positively associated with agricultural landscape evenness. The spider assemblage composition at the genus level was related to sampling method and plant species richness of understory plants within the orchards (local scale), and elevation of the orchard, annual rainfall at each site, and the proportion of the surrounding landscape occupied by perennial crops and urban areas (human-dominated) at the landscape scale. Local and landscape measures explained 17% and 12% of spider genus abundance data, respectively. II Acknowledgments I would like to express my sincere gratitude to my advisors Prof. Yael Lubin, Dr. Efrat Gavish- Regev and Prof. David Saltz for their continuous support throughout this research. I would like to especially thank Yael and Efrat who introduced me to the world of arachnology, Yael was always there for me when I needed any sort of guidance. I would like also to thank the following people: Iris Musli for help in spider identification, Ittai Renan and Dr. Phyllis G. Weintraub for help in insect identification, Prof. Tamar Keasar, Miriam Kishinevsky and Igor Armiach for help in field work, sampling collections and sharing thoughts, Dr. Arie Rosenfeld for landscape analysis data, Dr. Marjia Majer for map preparation, Prof. Stano Pekar for fruitful discussion and my committee members Dr. Ally Harari and Dr. Michal Segoli. And finally I would like to thank my family and especially my mother Suzi, for their support and for believing in me. III Table of contents Abstract………………………………………………………………………….. I Acknowledgments………………………………………………………………... II Table of contents ………………………………………………………………… III List of figures and tables……………………………..…………………………. V Chapter one…………………………………………………………………… 1 1.1. General introduction……………………………………………………………… 1 1.2. References ……………………………………………………………………….. 4 Chapter two ………………………………………………………………….. 7 2.1. Introduction……………………………………………………………………… 7 2.2. Methods………………………………………………………………………….. 11 2.2.1. Study sites ………………………………………………………………………. 11 2.2.2. Spider and insect sampling …………………………………………. 11 2.2.3. Data analysis…………………………………………………………………….. 12 2.2.3.1. Diversity measures: spider diversity; landscape data………………………. 13 2.2.3.2. Variables used in data analysis ……………………………………………... 14 2.2.3.3. Model selection ……………………………………………………………... 14 2.2.3.4. Hypotheses testing…………………………………………………………... 15 2.2.3.5. Edge effect and landscape diversity ………………………………………... 15 2.3. Results …………………………………………………………………………... 16 IV 2.3.1. Hypotheses testing ……………………………………………………………… 17 2.3.2. Productivity and habitat-heterogeneity hypotheses…………………………. 17 2.4. Discussion ………………………………………………………………………. 21 2.5. References ………………………………………………………………………. 23 Appendix A………………………………………………………………………. 30 Chapter three……………………………………………………………………. 33 3.1. Introduction…………………………..…………………………………………… 33 3.2. Methods…………………………………………………………………………….. 36 3.2.4. Variables used in data analysis …………………………………………………. 36 3.2.5. Multivariate analysis of spider assemblage composition………………………... 37 3.2.6. Functional groups ……………………………………………………………….. 38 3.3. Results ………………………………………………………………………….. 39 3.4. Discussion ………………………………………………………………………. 48 3.5. References ………………………………………………………………………. 53 Appendix B ……………………………………….……………………………. 57 Chapter four …………………………………………………………………….. 60 4.1. Concluding remarks ……………………..………………………………… 60 4.2. References ………………………………………………………………….. 63 V List of tables and figures Chapter two Figure 2.1: A map of the 12 pomegranate orchards distributed along the precipitation gradient……………………………………………………………………………………. 11 Table 2.1: The proportions of the four landscape types in pomegranate orchards. For each landscape type the minimum (%) and the maximum (%) represent the lowest and the highest proportion of each landscape types respectively among all pomegranate orchards……………………………………………………………………....................... 13 Table 2.2: List of variables used to explain spider diversity in pomegranate orchards in model selection. The minimum and the maximum represent the lowest and the highest value of each variable from all pomegranate orchards……………………………………. 14 Figure 2.2: Spider genus abundance for the 16 most dominant genera from all sampling sites combined showing the difference between the two sampling methods: trunk traps and canopy shaking…………………….............................................................................. 17 Figure 2.3: Figures represent combined data from the different sampling methods. A. Testing productivity hypothesis: scatter plots of spider diversity as a response variable against annual rainfall and insect abundance. B. Testing habitat heterogeneity hypothesis: spider diversity as a response variable and mean plant cover (%), plant richness, the percent of non-crop habitat and agricultural landscape evenness as independent variables. Agricultural landscape evenness was the only variable to show statistical significance (p<0.05)………………………….………………………………... 19 Table 2.3: Akaike information criterion (AICc) for the four best models selected to explain spider diversity from 12 pomegranate orchards distributed along climatic gradients. k: number of variables, ΔAICc: difference between the AICc of the considered model and the best model, Wi: Akaike weight. Models are ranked from best to worst. Predictor variables include ALE: agricultural- landscape evenness (Shannon’s evenness), latitude of the orchards and area of the orchards……………………………… 20 Table A1: The number of spider genera, total number of individuals, diversity index (Fisher’s alpha), and agricultural landscape evenness (ALE) expressed a Shannon’s evenness for each pomegranate orchard…………………………………………………... 30 Table A2: A complete list of all spider genera collected from pomegranate orchards using shaking canopies and trunk traps combined………………………………………... 31 Table A3: The 12 pomegranate orchards used in this study: the area of the orchards in meters, latitude, longitude and elevation of each orchard are provided. Orchards are ranked from the smallest in size to the largest……………………………………………. 32 Chapter three Table 3.1: List of variables used to explain spider assemblage composition in pomegranate orchards. The minimum and the maximum represent the lowest and the highest value of each variable from all pomegranate orchards…….................................... 37 VI Table 3.2: Partial CCA analysis on local scale variables. The significant variables, plant richness and sampling method, were included as a single explanatory variable and the effects of other variables were removed by defining them as co-variables. P- Value, F- ratio and the first eigenvalues are shown.............................................................................
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