Estimating and Classifying Spatial and Temporal Distributions of Flow Conditions for Fish Habitats by Using Geostatistical...
Outline<br />Introduction<br />Material & Methods<br />Results<br />Discussion & Conclusion<br />References<br />2<br />
Introduction<br />3<br />
Streams are heterogeneous environments where organisms exhibit patchy distributions on a spatially and temporally variable...
This study objectives:<br />(1)Simulate the velocity, depth and fish probability in the investigating reaches.<br />(2)Est...
6<br />Material & Methods<br />
7<br />Study area<br />Datuan stream (Fig. 1) has a<br />     total length of 14.5 km, and <br />     we choose four secti...
  Velocity and depth was measured by propeller-type current meter</li></li></ul><li>8<br />Kriging estimation<br />Kriging...
The Krigingestiamtion is divided into two sections :<br />(1)Ordinary Kriging is used to interpolating  <br />     velocit...
10<br />Flow classificationmethods<br />Flow conditions of estimated velocity and water depth values are classified by Fro...
The overlapping of the fish probability and flow condition maps.<br />Fish probability<br />+<br />Flow condition<br />fis...
Using the combination of flow condition maps and GIS, it could be easy to show the relation between the flow condition and...
Results<br />13<br />
Empirical method in winter<br />Reach<br />(1) <br />(2) <br />(3) <br />(4) <br />Pool and run occupied the stream area i...
15<br />Fig 3a. Reach (1)<br />Fig 3b. Reach (2)<br />Fig 3c. Reach (3)<br />Fig 3d. Reach (4)<br />Reach 1 in winter had ...
Froude Number method in winter<br />In Fig. 3e and f, the result was close to Fig. 3a and b, but part of run occurred in r...
17<br />Fig  3f. Reach (2)<br />Fig  3e. Reach (1)<br />Fig  3g. Reach (3)<br />Fig  3h. Reach (4)<br />There was a differ...
Empirical method in spring<br />The major flow conditions were pool in reach 1 and riffle in reach 2. And reach 2 had high...
19<br />Fig 4b. Reach (2)<br />Fig 4a. Reach (1)<br />Fig 4c. Reach (3)<br />Fig 4d. Reach (4)<br />The type of Fig. 4b wa...
Froude Number method in spring<br />The downstream was mainly categorized as pool and run. Riffle (Fig. 4b) was easy to be...
21<br />Fig 4e. Reach (1)<br />Fig 4f. Reach (2)<br />Fig 4g. Reach (3)<br />Fig 4h. Reach (4)<br />In reach 3, run is alm...
Discussion<br />22<br />
23<br />We combined the two classifications and kriging<br />      estimation in order to predicts the variability of stre...
24<br />(3) The result of the two classifications were not identical, <br />     especially in areas from downstream to mi...
25<br />Conclusion<br />
26<br />(1)These results not only describe the abundance and    <br />     heterogeneity of S. japonicusfrom downstream to...
References<br />1. Joanna, L.K., David M.H., Giuseppe A.C.: The habitat-scale ecohydraulics of rivers. Ecological Engineer...
~~~ Thanks for your attention ~~~<br />28<br />
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Estimating and Classifying Spatial and Temporal Distributions of Flow Conditions for Fish Habitats by Using Geostatistical Approaches with Measured Flow and Fish Data, Lin Yu-Pin, Wang Cheng-Long

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Estimating and Classifying Spatial and Temporal Distributions of Flow Conditions for Fish Habitats by Using Geostatistical Approaches with Measured Flow and Fish Data, Lin Yu-Pin, Wang Cheng-Long

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Estimating and Classifying Spatial and Temporal Distributions of Flow Conditions for Fish Habitats by Using Geostatistical Approaches with Measured Flow and Fish Data, Lin Yu-Pin, Wang Cheng-Long

  1. 1. Estimating and Classifying Spatial and Temporal Distributions of Flow Conditions for Fish Habitats by Using Geostatistical Approaches with Measured Flow and Fish Data<br />Advisor:Lin Yu-Pin , PH.D<br />Presenter: Wang Cheng-Long<br />1<br />Environmental and Landscape Ecological Lab<br />
  2. 2. Outline<br />Introduction<br />Material & Methods<br />Results<br />Discussion & Conclusion<br />References<br />2<br />
  3. 3. Introduction<br />3<br />
  4. 4. Streams are heterogeneous environments where organisms exhibit patchy distributions on a spatially and temporally variable physical arena<br />To improve the accuracy of flow conditions judgment, the habitat model identifying the flow conditionsare clearly illustrated by four key variables: water depth, water velocity, substrate composition and in-stream cover.<br />4<br />Velocity, depth, river cross-sections, slope, substrate…etc<br />Flow conditions (i.e. Pool, Riffle, Slack, Run)<br />Froude Number method<br />Empirical method<br />
  5. 5. This study objectives:<br />(1)Simulate the velocity, depth and fish probability in the investigating reaches.<br />(2)Estimate the flow condition requirement of S. japonicus, in order to find the preference of S. japonicusin reach scale.<br />(3)Discover the relation between the classifications of flow conditions and S. japonicusin the seasonal variations.<br />5<br />
  6. 6. 6<br />Material & Methods<br />
  7. 7. 7<br />Study area<br />Datuan stream (Fig. 1) has a<br /> total length of 14.5 km, and <br /> we choose four sections, <br /> which are investigated by <br /> 50m. in winter and spring.<br />Indicator species and measuring equipment<br /> S. Japonicus reproduces during the summer in the mid and upstream sections of rivers, then they return to the fresh water upstream approximately 6 months after hatching. <br />Fig. 1 Study area<br />50m<br /><ul><li>Fish collection was using the backpack electroshocker.
  8. 8. Velocity and depth was measured by propeller-type current meter</li></li></ul><li>8<br />Kriging estimation<br />Kriging estimating water velocity and water depth is to calculate experimental variograms.<br />The kriging estimation variance can be calculated by adopting the Lagrange method to minimize the estimation variance based on non-biased constraints.<br />(1)<br />(2)<br />
  9. 9. The Krigingestiamtion is divided into two sections :<br />(1)Ordinary Kriging is used to interpolating <br /> velocity/depth value in the unsampling sites.<br />(2)Indicator Kriging is applied to estimating the fish probability in the reach.<br />9<br />
  10. 10. 10<br />Flow classificationmethods<br />Flow conditions of estimated velocity and water depth values are classified by Froude number (Jowett, 1993) and the empirical method (Wong, 2000).<br />
  11. 11. The overlapping of the fish probability and flow condition maps.<br />Fish probability<br />+<br />Flow condition<br />fish probability map<br />flow condition maps<br />Fig. 2 overlapped mapping<br />11<br />
  12. 12. Using the combination of flow condition maps and GIS, it could be easy to show the relation between the flow condition and fish probability in topology in Datuan stream .<br />GIS exhibition<br />
  13. 13. Results<br />13<br />
  14. 14. Empirical method in winter<br />Reach<br />(1) <br />(2) <br />(3) <br />(4) <br />Pool and run occupied the stream area in reach 1,2 and 3<br />Run appeared at the most area of reach 4, but some pools were distributed in the middle section.<br />Fig. 3a<br />Fig. 3b<br />Fig. 3c<br />Fig. 3d<br />50m<br />14<br />
  15. 15. 15<br />Fig 3a. Reach (1)<br />Fig 3b. Reach (2)<br />Fig 3c. Reach (3)<br />Fig 3d. Reach (4)<br />Reach 1 in winter had only one type of flow conditions (pool). The area ratio of pool is reduced except that of the probability of 0~0.2.<br />Run was the only flow condition in reach 2 (Fig. 3b). In addition, the variation of area ratio was increased by the raising of probability.<br />Run occupied the area of reach 3 (Fig. 3c), the area of run was increased except for the probability interval of 0.8~1.<br />The area of run was greater than the other flow condition (pool). It means that run was one of the suitable habitats for S. japonicus.<br />
  16. 16. Froude Number method in winter<br />In Fig. 3e and f, the result was close to Fig. 3a and b, but part of run occurred in reach 1. <br />Pool and riffle also occupied the stream area in reach 3. The case differed from the result in the empirical method<br />Run still appeared at the most area of reach 4, but the range of pool distribution is more widely in the Froude number method<br />Fig. 3e<br />Fig. 3f<br />Fig. 3g<br />Fig. 3h<br />50m<br />16<br />
  17. 17. 17<br />Fig 3f. Reach (2)<br />Fig 3e. Reach (1)<br />Fig 3g. Reach (3)<br />Fig 3h. Reach (4)<br />There was a difference between the two classifications. Run (Fig. 3c) was replaced with pool and riffle (Fig. 3g), and the areas of pool and riffle were increased except for the probability of 0.8~1.<br />The type of Fig. 3e and f was similar to Fig. 3a and b with the difference of the appearance of run in reach 1 and 2.<br />The type of Fig. 3h was identical to Fig. 3d, but the area of pool was larger.<br />
  18. 18. Empirical method in spring<br />The major flow conditions were pool in reach 1 and riffle in reach 2. And reach 2 had high heterogeneity of the flow.<br />Run and riffle had the most two great area in the reach 3 and 4. The flow condition in reach 4 had high heterogeneity as same as which in reach 2.<br />Fig. 4a<br />Fig. 4b<br />Fig. 4c<br />Fig. 4d<br />50m<br />18<br />
  19. 19. 19<br />Fig 4b. Reach (2)<br />Fig 4a. Reach (1)<br />Fig 4c. Reach (3)<br />Fig 4d. Reach (4)<br />The type of Fig. 4b was similar with Fig. 3b, riffle was the most important flow condition in reach 2, and the area ratio of it increased.<br />Reach 3 was shared with run and riffle (Fig. 4c). The result was similar with Fig. 3c, but the area of riffle in spring was widely spread.<br />Pool owned the most of area in reach 1, but some area belonged to riffle.<br />Reach 4 had mixed flow conditions (pool, riffle, run, slack) (Fig. 4d)<br />
  20. 20. Froude Number method in spring<br />The downstream was mainly categorized as pool and run. Riffle (Fig. 4b) was easy to be identified with run (Fig. 4f) in reach 2.<br />Run covered the most part of reach 3 <br />There was a quite difference between the two flow classifications.<br />The result in the Froude number method was as same as the empirical rule method; besides, part of pool and riffle were inlayed in reach 4.<br />Fig. 4e<br />Fig. 4f<br />Fig. 4g<br />Fig. 4h<br />50m<br />20<br />
  21. 21. 21<br />Fig 4e. Reach (1)<br />Fig 4f. Reach (2)<br />Fig 4g. Reach (3)<br />Fig 4h. Reach (4)<br />In reach 3, run is almost the only flow condition, and the area of run increased except for the situation in the highest probability (0.8~1) (Fig. 4g)<br />In reach 1, the area of pool decreased when the probability increased (Fig. 4e).<br />Reach 2 had a mixed flow conditions which was similar to Fig. 3f, and run still owned the largest area in this reach (Fig. 4f).<br />There were three flow conditions (run, pool, riffle) in reach 4, especially, run got the most of area (Fig. 4h)<br />
  22. 22. Discussion<br />22<br />
  23. 23. 23<br />We combined the two classifications and kriging<br /> estimation in order to predicts the variability of stream conditions in a fish community, and discover its impact on the distribution in temporal scale.<br />(2) Two key factors, current velocity and stream depth, are <br /> the most two important factors in the habitat preference <br /> of fish. The pool/riffle series are usually related with <br /> rank erosion and the type of substrate.<br />
  24. 24. 24<br />(3) The result of the two classifications were not identical, <br /> especially in areas from downstream to middle stream <br /> under construction, and the classifications may also lose <br /> their accuracy due to the artificial disturbances.<br />(4)Base on the fish’s life cycle (shelter, reproduction, food source), the result shows that the empirical method is more appropriate for Datuan stream than the Froude number method.<br />
  25. 25. 25<br />Conclusion<br />
  26. 26. 26<br />(1)These results not only describe the abundance and <br /> heterogeneity of S. japonicusfrom downstream to <br /> upstream, but also quantify the area ratio of the combination <br /> of fish probability and flow conditions in each reach.<br />(2) These outcomes reduce the cost in time and money, then<br />provide ecological information for engineers to river <br /> restoration, which supply the suitable habitats for the life-<br /> cycle of S. japonicus.<br />
  27. 27. References<br />1. Joanna, L.K., David M.H., Giuseppe A.C.: The habitat-scale ecohydraulics of rivers. Ecological Engineering 16 (2000) 17-29<br />2. Wong, C.M.: Water resources education at Da-Chia stream, Water Resources Agency, Ministry of Economic Affairs (2000) 30-<br /> 45<br />3. Francisco, L., Lilian, C., Helena, S.G., Andre, B.D.C., Denise, D.C.R.F.: Riffle and pool fish communities in a large stream of <br /> southeastern Brazil. Neotropical Ichthyology 3(2) (2005) 305-311.<br />4. Azzellin A., Vismara R.: Pool Quality Index: New Method to Define Minimum Flow Requirements of High-Gradient, Low-<br /> Order Streams. Journal of Environmental Engineering, 127 (11) (2001)<br />5. Deborah S., Dan R.: Hyfraulic habitat composition and diversity in rural and urban stream reaches of the north Carolina <br /> Piedmont(USA). River. Res. Applic. 24 (2008) 1082-1103.<br />6. Jowett, I.G.: A method for identifying pool, run, and riffle habitats from physical measurements. New Zealand, J. Mar. <br /> Freshwater Res. 27 (1993) 241-248<br />7. Durance, I., Lepichon, C.,Ormerod, S.J.: Recognizing the importance of scale in the ecology and management of riverine fish. <br /> River Research and Application 22 (2006) 1143-1152.<br />8. Torgersen C.E., Gresswell R.E., Bateman D.S.: Pattern detection in stream networks: quantifying spatial variability in fish <br /> distribution. In: Proceedings of the Second Annual International Symposium on GIS/Spatial Analyses in Fishery and Aquatic <br /> Sciences (Eds T. Nishida, P.J. Kailola & C.E. Hollingworth).Japan, Fishery GIS Research Group, Saitama (2004) 405-420<br />9. Wang, Y.C., Lin, Y.P., Cho, T.H., Wang, C.L.: Estimating scale-dependent hierarchical variations and longitudinal distribution <br /> of stream fish abundance--Datun stream, Taiwan, International Statistical Ecology Conference 2008, Scotland (2008)<br />10. Lin, Y.P., Yeh, M.H., Deng, D.P., Wang, Y.C.: Geostatistical Approaches and Optimal Additional Sampling Schemes for <br /> Spatial Patterns and Future Samplings of Bird Diversity. Global Ecology and Biogeography 17(2008) 175-188<br />11. Lin, Y.P., Chen, B.Y., Shyu, G.S., Chang, T.K.: Combing a Finite Mixture Distribution Model with Indicator Kriging to <br /> Delineate and Map the Spatial Patterns of Heavy Metal Pollution in Soil. Environmental Pollution 158 (2010) 235-244<br />12. Carroll, S.S., Pearson, D.L.: Detecting and modeling spatial and temporal dependence in conservation biology. Conservation <br /> Biology 14 (2000) 1893-1897<br />13. Cressie, N. A. C.: Statistics for spatial data, revised Edition edn. Wiley 506 Inter-science, New York (1993)<br />27<br />
  28. 28. ~~~ Thanks for your attention ~~~<br />28<br />

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