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Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
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He has been selected among the top 2% scientist in world according to Stanford University report for 2020.",institutionString:"Egyptian Atomic Energy Authority",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"10",totalChapterViews:"0",totalEditedBooks:"10",institution:{name:"Egyptian Atomic Energy Authority",institutionURL:null,country:{name:"Egypt"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"14",title:"Materials Science",slug:"materials-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"297737",firstName:"Mateo",lastName:"Pulko",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/297737/images/8492_n.png",email:"mateo.p@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"2383",title:"Polyester",subtitle:null,isOpenForSubmission:!1,hash:"79fd9d6314f8e1abd60d7e21896ce878",slug:"polyester",bookSignature:"Hosam El-Din M. Saleh",coverURL:"https://cdn.intechopen.com/books/images_new/2383.jpg",editedByType:"Edited by",editors:[{id:"144691",title:"Prof.",name:"Hosam",surname:"Saleh",slug:"hosam-saleh",fullName:"Hosam Saleh"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5242",title:"Management of Hazardous Wastes",subtitle:null,isOpenForSubmission:!1,hash:"cc1f32b478098cdda6b946d14a02ad81",slug:"management-of-hazardous-wastes",bookSignature:"Hosam El-Din M. Saleh and Rehab O. 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Saleh and Martin Koller",coverURL:"https://cdn.intechopen.com/books/images_new/6847.jpg",editedByType:"Edited by",editors:[{id:"144691",title:"Prof.",name:"Hosam",surname:"Saleh",slug:"hosam-saleh",fullName:"Hosam Saleh"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8000",title:"Assessment and Management of Radioactive and Electronic Wastes",subtitle:null,isOpenForSubmission:!1,hash:"0195aa3bce1f0c8783649a32a4affeaf",slug:"assessment-and-management-of-radioactive-and-electronic-wastes",bookSignature:"Hosam El-Din Saleh",coverURL:"https://cdn.intechopen.com/books/images_new/8000.jpg",editedByType:"Edited by",editors:[{id:"144691",title:"Prof.",name:"Hosam",surname:"Saleh",slug:"hosam-saleh",fullName:"Hosam Saleh"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7640",title:"Perspective of Carbon Nanotubes",subtitle:null,isOpenForSubmission:!1,hash:"8b85a9957fad5206369eadf0c1ffa27d",slug:"perspective-of-carbon-nanotubes",bookSignature:"Hosam El-Din Saleh and Said Moawad Mohamed El-Sheikh",coverURL:"https://cdn.intechopen.com/books/images_new/7640.jpg",editedByType:"Edited by",editors:[{id:"144691",title:"Prof.",name:"Hosam",surname:"Saleh",slug:"hosam-saleh",fullName:"Hosam Saleh"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"70366",title:"Solving Partial Differential Equation Using FPGA Technology",doi:"10.5772/intechopen.84588",slug:"solving-partial-differential-equation-using-fpga-technology",body:'\nSolving the partial differential equation (PDE) has been investigated by many researchers, implementing digital decoding on PCs successfully. However, with the problem of large computing space, the resolution on the PC is difficult to meet the requirements of speed and accuracy calculations; in some cases, the problem cannot be solved because of the calculation. Cellular Neural Network technology (CNN) researchers have applied cellular neural network (CNN) technology successfully to perform analysis of the problem, design CNN chip, and solve some PDEs.
\nUsing CNN technology for solving PDE, we have to analyze and difference the original particular equations of problem, find templates, design CNN architecture, and then configure FPGA to make a CNN chip. It means that there is no CNN chip for every equation, but for each problem (consist of some equations), there is need to design appropriate CNN chip. When solving large problems, computing resources are needed to configure blocks of CNN chips. In order to save resources, we have proposed a solution for dividing computing space into smaller subspaces and composite parallel and sequential calculations, which ensures high computing rates but saves resources of FPGA chips used.
\nBecause the architecture of CNN chips varies depending on each problem, making the CNN chip is very difficult and costly with traditional methods. Using the FPGA technology, users can use hardware programming languages, such as Verilog and VHDL, to configure the logic elements in the FPGA to produce the electronic circuit of a CNN chip. The recent FPGA architectures (Virtex 7; Stratix 10) have many tools support to test, optimize, and coordinate data exchange. The CNN designer should use FPGA for making a CNN chip.
\nCellular neural network (CNN) was introduced by Chua and Yang at Berkeley University, California (USA), in 1988, which combined both analog spatial temporal dynamic and logic [1, 2, 3]. The CNN paradigm is a natural framework to describe the behavior of locally interconnected dynamic systems, which has an arrayed structure, so it is very useful in solving the partial differential equations [3, 4, 5, 6, 7]. Today, visual microprocessors based on this processing type can perform at TeraOPs computing power and approximately 50,000 fps. The possibility of developing algorithms and programs based on CNN was quickly exploited worldwide. Up to now, there are several CNN models for processing images, solving PDE, recognizing pattern, gene analysis, etc. Depending on problems, the designer can make a CNN chip having size of millions cells. The common CNN architectures are 1D, 2D, and 3D.
\nThe standard CNN 2D is the dynamic system of autonomous cells that are connected locally with its neighbor forming a two-dimensional array [2, 18]. Each cell in the array C(i,j) contains one independent voltage source, one independent current source, a linear capacitor, resistors, and linear voltage-controlled current sources which are coupled to its neighbor cells via the controlling input voltage, and the feedback from the output voltage of each neighbor cell C(k,l). The templates A(i,j;kl) and B(i,j;k,l) are the parameters linking cell C(i,j) to neighbor C(k,l). The effective range of Sr(I,j) on radius r of cell C(I,j) is identified by the set of neighbor cells which satisfies (Figure 1).
\nThe architecture of a CNN chip.
The state equation of cell C(i,j) is given by the following equation:
\nWith R, C is the linear resistor and capacitor; A(i,j;kl) is the feedback operator parameter; B(i,j;kl) is the control parameter; and zij is the bias value of the cell C(i,j). On the CNN chip, (A, B, z) are the local connective weight values of each cell C(i,j) to its neighbors. The output of the cell C(i,j) is presented by Yij as:
\nThe characteristic of the CNN output function Yi,j = f(xij) is presented in Figure 2.
\nCNN circuit output function.
On the CNN 3D, beside connection with neighbors, the cell has other connection to upper and lower layer in the three-dimensional space [18] as shown in Figure 3. Thus, if radius r = 1, the cell C(i,j,k) has 26 neighbors; hence, the templates A and B have more three coefficients A(i,j,k) and B(i,j,k).
\nThe 3D CNN, with r = 1, (having 26 neighbors) in three dimensions coordinates x,y,z.
The state equation of CNN 3D takes the form:
\nThe output function is similar to CNN 2D:
\nFor the problem-solving of three-dimensional PDE, the CNN 3D must be used. The original PDE is differentiated and from that the appropriate templates (A,B,z) of the CNN 3D are generated.
\nField-programmable gate array (FPGA) is the technology in which the blank blocks have available resources of logic gates and RAM blocks are used to implement complex digital computations. FPGAs can be used to implement any logical function. The FPGA block is able to update the functionality after shipping, partial reconfiguration of a portion of the design, and the low nonrecurring engineering costs relative to an ASIC design [13, 14, 15, 16].
\nA recent trend has been to take the coarse-grained architectural approach by combining the logic blocks and interconnects of traditional FPGAs with embedded chips and related peripherals to form a complete “system on a programmable chip” [17, 18, 19].
\nUsers like teachers and students could use FGGA for making prototypes for testing application system, with VHDL or Verilog users easily design and test and then reconfigure the system until it has desired results.
\nBecause the CNN architecture is not the same for every application, based on the standard model, the designer develops a particular chip for each problem. FPGA is the most useful for configuring a blank chip to make a CNN chip using programming language like Verilog or VHDL. For solving PDE, firstly, one needs to analyze (differencing) the original model of partial differential equations for finding appropriate template, then base on template found designing architecture CNN chip, finally, using VHDL to configure FPGA following designed hardware making CNN chip.
\nSome PDEs have been solved using the CNN technology:
\nBurger equation [3]:
\nKlein-Gordon equation [19]:
\nHeat diffusion equation [3]:
\nBlack-Scholes equation [9]:
\nAir pollution equation [4]:
\nSaint venant 2D equation [5]:
\nSaint venant 1D equation [6]:
\nExample of making a CNN chip for solving Saint venant 1D:
Designing the templates
First, changing the original equation (4)
\nand then choosing the difference space of variables x with step Δx for right part of (6). After differencing only the right side of (6) for space variable x by Taylor expansion, one has equation for cell at position (i):
\nNote that, following the CNN algorithm, on the left, we do use symbol (\n
where Rh is the linear resistance on cell circuit of h.
\nFor Eq. (5), changing slightly with assumptions above:
\nAssume that q > 0, then kq = 0. After differencing, applying the template design algorithm of CNN, one can has templates for (8):
\nFrom template found, we can design the CNN architecture for problem as (1) two layered-1D CNN chip (Figure 4) and (2) the h, Q processing block (Figure 5).
\nLogical architecture of a CNN cell.
Logical architecture of a h, Q cell.
The cell is mixed both of h, Q in one block to make the physical architecture of a CNN cell.
\nIn general, for each calculation, we need some basic computing block like ADDITION, SUBTRACT, MULTIPLE, DIVIDE. When designing a CNN cell using FPGA, one has to design many separate blocks of them to perform arithmetical processing for each input. In order to save computing resource in FPGA, the method that shares basic block in one cell leading to sequential calculating can be used (Figure 6). In this case, the processing time of each cell will be high. To reduce the processing time of each cell, we can use a pipeline mechanism shown in Figure 7, but it needs more computing resource for each cell. Finally, for cells in a CNN chip, we process parallel as in Figure 8.
\nPhysical architecture of CNN cell.
Solution for physical architecture CNN chip.
A core architecture for CNN chip.
C1, …, C4 are the coefficients as shown in Figure 7, (C1= \n
If each cell is uses a pipeline mechanism shown in Figure 7. With the length of a pipeline is 6, the first calculation pays 6 clock pulse (clk), and each calculation after that only needs 1 clk.
\nIn hydraulics, many flow models have been researched, such as flows in channels, streams, or rivers, for controlling the flow for preventing disasters, saving water, and exploiting energy of the flow as well. Most of mathematical models of those phenomena are partial differential equations like Saint venant equations and Navier-Stokes equations [8, 9]. Some types of Navier-Stokes equations have various parameters and constraints. Using CNN technology, we could solve some of them which have clear values of boundary conditions; it means we do not research boundary problems deeply. The effectiveness of the CNN technology is making a physical parallel computing chip to increase the computing speed for satisfying a real-time system.
\nNavier-Stokes equations here consist of three partial differential equations, with functional variables representing water height, and flow velocity in x- and y-directions. The empirical model is a flow through a small port, which diffuses in two directions Ox and Oy.
\nSolving Navier-Stokes equations by using CNN requires the discretion of continuity model by difference method, the smaller difference intervals the higher accuracy. However, if difference intervals are too small, then it leads to increasing the calculation complexity and time. The CNN chip with parallel physically processing abilities, the above difficulties will be overcome.
\n\n
Equations describing the water level
Assume that the height of water is taken from the bottom of the flow, which is regarded as the origin of the coordinate system, so zw has no negative values.
Momentum equations in x-direction:
\n
Momentum equations in y-direction:
Explain the meanings of quantities in the equations:
\n\n
\n\n
\n\n
\n\n
\n
\n\n
where:
\nWith cs1; cs2; Wmin are values get from practical, for example: Wmin = 4 m/s; wind speed is 10 m/s, then cs1 = 1.0; cs2 = 0.067;
\n\n
Expressions, \n
To simplify, change parameters as: the water level zw = h; and the velocity in x-axis qx = u, in y-axis qy = v. Assume that qA = 0; the kinetic influence of turbulent values between velocity in the direction from 0y to 0x (or 0x to 0y) is trivial since horizontal velocity is small enough to be considered as zero; then (9)–(11) are rewritten:
\nStep 1: Differencing equations following Taylor formula
\nUsing finite difference grid with difference interval in x-axis as \n
Step 2: Designing a sample of CNN
\nBased on CNN state equations and difference equations (15)–(17), we can have CNN templates for layers h, u, v:
Layer h:
\n
Layer u:
\n
Layer v:
Step 3: Designing hardware architecture of CNN to solve Navier-Stokes equations
\nBased on templates found in (18)–(20), we can design an architecture for circuit for CNN chip. It is a three-layered CNN 2D. Then, the arithmetic unit for each layer and links to perform parallel calculation on chip can be made. Figure 9 shows the architecture of layer h and layer u (the layer v is similar to u).
\nLogic architecture of cell of h, u.
The empirical problems that need a solution is that: firstly, identifying boundary points of whole difference grid (space); secondly, dividing the entire computing space into smaller subspaces. Division and combination of boundary areas need to perform appropriately avoiding incorrect results because of tep time computing time; thirdly, controlling real-time data exchange and combining sequential and parallel computing in a CNN chip. The CNN chip proposed in this chapter has solved similarity in the previous problems [4, 5]. The new issues here are dividing computing space processing dynamic sub-boundary and combining sequential and parallel.
\nEach CNN cell has its own data element and a core that performs the computing function. The CNN has MxN CNN cells in which only (M-2)x(N-2) CNN cells have computing functions, so that the CNN has MxN data elements and (M-2)x(N-2) cores (Figure 10).
\nGeneral architecture of a CNN chip.
The Buffer supplies MxN data elements for CNN. Each MxN data element is called as one block of data (Figure 11).
\nBuffer (MxN) for CNN core.
The white area is the data element for CNN boundary cells; and the gray part is the data area which requires to be processed by CNN. The CNN arithmetic unit has size of (M-2)x(N-2) cells processing data for the gray area which is inside the input buffer unit.
\nThe Input memory has PxQ blocks of data. It is a true dual port memory.
\nThe Temp memory also has PxQ blocks of data. It is a simple dual port memory. It is used to temporarily store data computed from CNN core and supply data for Boundary updating unit.
\nData that need processing sent from PC have the size of mxn (Figure 12).
\nComputing space with main boundary.
Assume that m = 5, n = 6, M = 3, and N = 4; the white part is boundary and the gray part is the area requiring to be processed. Before the processing data, temporary vertical and horizontal boundaries be need to be added, as in Figure 13, column (0,3) and row (3,0).
\nDivide computing space into subspace with subboundary.
Temporary vertical and horizontal boundaries are added to the data structure similar to CNN buffer. The data after being added from temporary vertical and horizontal boundaries will be sent to Input memory. The blocks of data in the Input memory unit (in case that mxn = 5x6, MxN = 3x4) are detailed as follows (Figure 14).
\nThe blocks of data in the Input memory in case that mxn = 5x6, MxN = 3x4.
0, 1, 2,.., 6 are the addresses of blocks. In case that mxn = 5x6 and MxN = 3x4, we have P = 3 and Q = 2.
\nThe Boundary updating unit is in detail structure as follows (in case MxN = 3x4) (Figure 15).
\nThe Boundary updating structure (MxN = 3x4).
The control unit controls the activities of the whole system set by the algorithm which is as follows: (1) At every posedge of clk do(2) {(3) if (has IO event)(4) do the IO task;(5) else(6) buffer = read(Input memory)(7) if (finish computing the first block)(8) if (BoundaryUpdating())(9) write(Input memory)(10) }
\nThe 3xN CNN architecture is similar to the general MxN CNN architecture (M = 3). In order to reduce the memory consumption and simplify the Boundary updating unit, there are some differences (Figure 16).
\nThe architecture of 3xN CNN chip.
Each block of data in the memory (Input memory or Temp memory) is 1xN data elements. Assume that the data which need processing sent from PC has the size of mxn, m = 5, n = 6, and assume that N = 4. As mention above, the data will be processed after temporary vertical boundaries are added; so that, the Input Memory unit will has 5x2 blocks of data (m = 5, Q = 2) as follow (Figure 17).
\nThe memory with 5x2 blocks (m==5, n = 6, N = 4).
Each block has size of 1x4 data elements.
\nThe Buffer unit is a Shift up register that has size of 3xN. The input and output have sizes of 1xN and 3xN, respectively. The input is at the bottom.
\nThe Input memory has m rows and Q columns of blocks of data. The control unit reads the blocks in the Input memory by vertical and puts the block of data to the input of buffer. The buffer shifts up 1 step. After step 3, the Buffer has 3xN blocks of data to supply to CNN core. After each step, the Buffer has 3xN blocks of data that need to supply to CNN core (Figure 18).
\nThe Buffer’s state after each step (m==5, n = 6, N = 4).
The output of CNN core has the size of 1xN.
\nThe Boundary updating unit is shown in Figure 19.
\nThe output size of CNN core (N = 4).
The control algorithm for control unit (Figure 20).(1) At every posedge of clk do(2) {(3) if (has IO event)(4) do the IO task;(5) else(6) buffer = read(Input memory);//read by vertical(7) if (finish computing the first block of column q)(8) if (column_of_current_block==0) write(Temp memory); else BoundaryUpdating(CNNoutput,read(Temp memory));(9) write(Input memory);(10) }
\nThe Boundary updating structure (N = 4).
The chip Virtex 6 (XCVL240T-1FFG1156) connected to PC for configuring to make CNN chip and performing calculation.
In this part, we implement the 3xN CNN. Q, m, and N are the parameters that we can configure before compiling and programming to the FPGA chip. For defaulting, we assigned Q = 2, m = 8, and N = 4.
\nFor experiencing, the ISE Design Suite software version 14.7 and ML605 evaluation board including chip XCVL240T-1FFG1156 (Virtex 6) are used to implement the schematic of CNN.
\nFirst, we use Verilog HDL language to describe the CNN architecture. Then, we use ISim simulator to verify our system. Finally, we program the system to the FPGA chip on ML605 board.
\nThe image of experience system as in Figure 20 is as follows.
\nThe input of CNN to solve the Navier-Stokes Equation has h, u, v values. We use three Input memory units, three Buffer units, and three Temporary memory units to store h, u, v values. The data element is represented in 32-bit floating point real numbers. Data into h, u, v are added with temporary boundaries, detailed as follow (presented in Decimal and Hex of Single-type Floating-point) (Figure 22).
\nInitial data for the Input memory h, u, v.
The interface of each Input memory, Temporary memory for h, u, v is configurated as same in Figure 23. The initial data for the Input memory h, u, v is initialed by COE files. A COE file stores initial values for a memory (Figure 24).
\nInterface for Input and Temp memory h, u, v.
An example of h.core file to initial data for the Input memory h.
\n
\n
\n
The interface of Control unit is described as follows.
To verify the system, the interface of the top module of the system should include all the signals that we want to verify.
\nThe top module is described as follows.
The ISE design software shows the device utilization summary as in Table 1.
\nDevices used summary (estimated values) | \n|||
---|---|---|---|
Logic utilization | \nUsed | \nAvailable | \nUtilization | \n
Number of slice registers | \n3952 | \n301,440 | \n1% | \n
Number of slice LUTs | \n16,365 | \n150,720 | \n10% | \n
Number of fully used LUT-FF pairs | \n1770 | \n18,547 | \n9% | \n
Number of bonded IOBs | \n3112 | \n600 | \n518% | \n
Number of Block RAM/FIFO | \n12 | \n416 | \n2% | \n
Number of BUFG/BUFGCTRLs | \n1 | \n32 | \n3% | \n
Number of DSP48E1s | \n132 | \n768 | \n17% | \n
Device utilization summary.
Figures 25–27 show the schematics synthesized by the ISE design software.
\nThe architecture of CNN chip.
The architecture of one CNN cell.
Inside electronic circuit for h.
Comparing the new values of h in Figure 28i, k (doutH) with Figure 29, we can see that the 3x4 CNN system worked well.
\nSignals operating inside the 3x4 CNN system, m = 8, Q = 2. (a) Starting a computing cycle by setting start = 1. (b) The output of Input memory (doutH). (c) The data outputting from Buffer after 4 clks. (d) The results from CNN core after 10 clks; and start writing the results to Temp memory. (e) The CNN core finish computing the first column of blocks of data at 16 clks; and pause writing the results to Temp memory at 16 clks. (f) The results from CNN core after 18 clks; read Temp memory, start updating boundaries, and write the results to Input memory. (g) Pause updating boundaries from 24 clks. (h) The CNN core finishes computing; read the last column of blocks of data from Temp memory and write to Input memory. (h) Finish writing all results of the first computing cycle to Input memory. (i) The controller sets finish = 1 at 33 clks. (k) The output of Input memory shows the results computed at previous computing cycle. (l) The overview of signals.
The new values of h computed by excel for the first computing cycle.
The simulation results show the properness and effectiveness of installation methods. The cost for calculating the first three blocks of 1xN taken from memory units h, u, v is 10 clock pulses, of which 1 clock pulse is for initial reading Input memory, 3 clock pulse is for initial updating buffer to CNN, and 6 clock pulses for initial calculation. Each successive 1xN unit takes only 1 clock pulse to calculate, due to the use of the pipeline mechanism to update buffer to CNN and calculate at CNN arithmetic unit. After finishing reading each column of blocks of data in the Input memory, it needs 2 more clocks for initiating the buffer again. It also takes 1 clk for initial writing Temp memory, 1 clk for initial reading Temp memory, and 1 clk for initial writing result back to Input memory.
\nAs a result, the time for one computing cycle is:
\nAs the above implementation, m = 8, Q = 2, and T = 32 (clk).
\nThis chapter gives the solution for configuring CNN chip to solve Navier-Stokes equations, especially concerning to solution in the temporary boundary problem when it is required. The purpose is to divide the big data space into many subspaces. The processing of the big data space is based on the calculation of each subdata. With the input data of 32-bit floating point real number and FPGA chip Virtex 6 XCVL240T-1FFG1156, the CNN of 1x12 cells has successfully installed. The installation results show that the effectiveness of this solution mainly lies on the expansion of calculation space and resource saving and the accuracy of the calculation acceptable as well. This model can be further developed to feasibly solve similar problems in larger computing space and could be developed for some types of complicated (mixed) boundaries as well.
\nWe would like to deeply acknowledge Professor Roska Tamas, the head of the Analogic and Neural Computing Research Laboratory and Chairman of the Scientific Council—Institute of the Hungarian Academy of Sciences; and Associate Professor Pham Thuong Cat, the Head of Automation Laboratory—Institute of Information Technology—Vietnam Academy of Science and Technology, for giving us many important instructions.
\nBehaviour is an animal phenotype and could be considered as a variable of adjustment for an animal to changes of environmental factors. Domestication gives new environmental conditions to animals; they have to adapt to these restricted surroundings. In general, captive conditions are less complex than those of a natural environment but even with less complexity, the environmental conditions of farms or other rearing structures could appear as new for animals. So they have to adapt. As a phenotype, behaviour is certainly the mean and the most useful to survive under the new conditions. So during the domestication process, behaviour allows the animal to adapt to its new environmental conditions. Through domestication, the artificial selection is a process of changing characteristics of animals by artificial means such as directional selection, familial selection [1] or genomic selection [2], and the domestication may impact the behaviour even after only one generation [3, 4].
\nBehavioural traits are among the first traits to be affected by domestication [5, 6]. Behaviour is more easily moulded than morphology or chemical composition and thus the costs of behavioural modification are more efficiently adjusted to environmental variations. In his book, Jensen [7] described the effects of domestication in vertebrates, mainly on birds and mammals but there was nothing on fishes. Before that, there were three major reviews [3, 4, 8] on the influence of aquaculture and domestication on fish behaviour. In these papers, the authors summarised most of the available information on the effects of domestication on different traits of fish behaviour. The major aim of these reviews was to consider the importance of behavioural modifications due to domestication on the economic interest of the culture of fishes and on the welfare of animals in fish farms. In this chapter, I focus on the behavioural traits that have been modified by domestication without consideration to either economic objectives or animal welfare.
\nThere are many difficulties to analyse papers dealing with the effects on domestication. First, it is not easy to identify precisely neither the number of generations in captivity nor the link between captive and wild animals. It is easy when it concerns the first generation obtained in captivity, but it is more complex when we address to ‘individuals reared in hatcheries’ for several years. Most often, we do not know if there was time introduction of wild animal (e.g. males) during the domestication process. Second, in most studies comparing wild and domesticated strains, we have very few information on the characteristics of the wild animals and on those of their native sites. It is important because there is an important variability of the behavioural trait parameters between different populations. Third, in general, fish performances of behavioural traits are tested under laboratory conditions except for displacements for which some experiments were realised in natural water areas. So whatever the experimental sites, the foreigner population (wild or domesticated) needs a period of acclimation to its new rearing conditions. These could introduce a bias in the results.
\nBehaviour is the basis of all relationships between the animal and its environment and concerns with several behavioural traits: swimming, foraging, predator avoidance, relationships with conspecifics and reproduction. Moreover, it is now known that individuals exhibit behavioural or physiological characteristics, which, if they are consistent over time, define a coping style or personality [9]. As through domestication, human beings select some individuals among a population, this could modify the equilibrium between the different behavioural profiles (or coping styles) of the individuals of a population. Now, some researches integrate this individual component and highlight the effects of domestication on individual behaviour as it has recently been done considering the learning and other cognitive abilities of fish.
\nIn this chapter, I will review some of these behavioural traits in hatchery-reared fishes that have often been altered in a characteristic manner by domestication.
\nSwimming is a general behavioural trait, which is used in different situations: foraging activity, predator avoidance, stress responses or reproduction. For fish, one of the most determinant traits that are able to improve foraging is the swimming ability. In rearing conditions, swimming is no longer as important as in nature; in general, fish have less space at their disposal, but if domestication selects individuals on their morphological and physiological characteristics, this could influence directly their swimming performances.
\nThis behaviour trait has been tested on fishes in response to a predator attack. It is the case for juveniles (between 55 and 125 days old) of the sea bass (Dicentrarchus labrax); wild individuals showed a greater angular velocity and a stop distance to a new object more important than reared fishes [10]. These responses decrease with habituation in both groups. It means that wild individuals have a greater reactivity and a longer escape distance from an unknown object in their environment.
\nIn the context of swimming behaviour, one of the more common tested parameter is the C-start response: this is the ability of an individual to rest from a novel environmental situation; it is characterised by a rapid reaction of the body with a C posture and after an S followed by a rapid (less than 10 ms) displacement. It measures the physical ability of a fish to react to a stress situation by using its physical abilities to swim. It has been tested in different environmental situations: pollution [11], water temperature [12], hypoxia [13] or the influence of conspecific presence by comparing solitary and grouped individuals [14]. In all cases, wild fishes showed a greater velocity and more rapid swimming abilities, so it seems that domestication decreases the swimming performances of the fish. This decrease could be parallel to physiological events. Comparisons of swimming and metabolic physiology were done in aquaculture-reared California yellowtail (Seriola dorsalis) in comparison to wild individuals. Incremental swimming velocity trials showed that aquaculture-reared fish had a significantly slower mean maximum sustainable swimming speed (4.16 ± 0.62 Body Length s−1) in comparison to that of wild fish (4.80 ± 0.52 BL s−1). In addition, oxygen consumption was significantly higher in aquaculture-reared fish (7.31 ± 2.32 vs. 3.94 ± 1.60 mg O2 kg−1 min−1 at 18°C) in comparison to wild-caught yellowtail (15.80 ± 5.78 mg O2 kg−1 min−1) [15].
\nThis could alter other behaviours, which depend directly on swimming (i.e. foraging, survival). One point that concerns with swimming performances is the ability for reared individuals to be released in wild sites. This is the case for the European grayling (Thymallus thymallus) that were tagged with radio-transmitters and tracked in the Blanice River, River Elbe catchment (Czech Republic) [16]. Wild and hatchery-reared fish increased their dial movements and home range with environmental variables (light intensity, flow, temperature and turbidity), but hatchery-reared fish displayed greater total migration distance than did wild fish, which was caused mainly by their higher dispersal. Patterns in space use and activity were compared for wild and hatchery-reared Mulloway (Argyrosomus japonicus) using acoustic telemetry. Adult individuals were followed during 288 h in a river. Hatchery-reared fish used significantly larger areas with higher rate of activity than wild fish, but their movement ranges were more variable [17] than those of wild fish. By comparing initial movement, habitat use, growth and mortality between stocked hatchery and wild fish of juveniles of Florida Bass (Micropterus floridanus) with a radio telemetry experiment, Thomson et al. [18] showed that tagged hatchery fish exhibited greater movement (75 and 124 m/d, respectively), greater proportion of locations offshore (8 and 23%, respectively), but slower growth (1.73 and 0.41% of their body weight gained per day, respectively), and higher predation (47 and 0%, respectively) than wild fish.
\nThese results showed that domestication can not only be influenced through selecting the physical characteristics of the individuals, but also through their swimming performances and consequently the foraging and space use by hatchery-reared individuals when released in wild conditions.
\nForaging is not only the activity, which consists to take off resources in the environment, that is, prey, but also the choice of the best site or the most favourable period where and when to forage. The animal must be at the good place at the best moment. This aim seems easy for animals in controlled environments where the food is abundant and regular; but this fact could be a disadvantage when aquaculture-reared fish are released in natural environment in order to supply the low level of the wild stocks.
\nFishes change their foraging habits with domestication. Zebra fish (Danio rerio) and coho salmon (Oncorhynchus kisutch) change the place where they forage after domestication after just one generation. Domestic fishes swim at the surface of the water column instead of the lower part for wild animals [19, 20]. One of the consequences is that farmed animals had a higher rate of prey capture than their wild congeners [21, 22]. These changes in foraging behaviour could be the result of changes in the relation of the fishes with its environment: as the predation rate was lower for farmed fishes, they adopt a more risky behaviour near the surface; the farmed conditions modified also the social relationships between individuals and could result in a lower influence of dominance in the foraging behaviour [23].
\nPerhaps, the main difference is that the natural environment provides a lot of different situations to which fishes have to adapt. It seems that the environmental complexity of natural environments may facilitate training to different situations [24], with a more important prey variability [25, 26, 27] or opportunity of social learning [28]. Consequences could be measured when farmed fishes were realised into natural environment: they use less of natural objects such as stones or leaves for digestion than wild animals [25] or they make no difference between prey of different profitability [26] and they do not choice an unknown prey [27].
\nThe conditions of foraging allow the fish to get a certain amount of resources from the environment and could explain important differences between hatchery-reared and wild individuals in terms of survival and growth. If we compare the survival rate of aquaculture-reared or wild Chinook salmon fry (Oncorhynchus tshawytscha) facing predation by rainbow trout (Oncorhynchus mykiss) or sculpin (Cottus rhotheus) under experimental conditions, wild fry had a survival advantage within the two next years of experiment [29]. So it is possible that the domestication can affect the vulnerability of juveniles of salmon after only one generation in a culture system. But it is not always the case. For example, the survival of Atlantic salmon (Salmo salar) in the Baltic Sea was examined in relation to the origin, and prey fish abundance (here herring Clupea harengus and sprat Sprattus sprattus). The study was based on recapture data for tagged hatchery-reared, and wild smolts demonstrated a combined influence of origin and environmental factors on survival; prey fish abundance had no influence on the survival of reared or wild smolt groups [30]. The results suggest that some larger smolt of the reared groups compared with the wild groups compensated for their lower ability to live in the wild.
\nThe anti-predator behaviour is highly sensitive to artificial rearing and so to domestication [12, 31, 32, 33, 34, 35, 36]. Anti-predator behaviour is thought to change during domestication, along with other traits. One prediction is that domestication should reduce behavioural responses to predation risk. This prediction was supported by a lot of studies most of the time on salmonids, on rainbow trout (Oncorhynchus mykiss) [31, 32], on brown trout (Salmo trutta) [12] and on Atlantic salmon (Salmo salar) [35, 37].
\nIn wild population, decreased activity, spatial avoidance of risky areas and the use of refuges reduce the rate of mortality caused by predators [38, 39]. This natural reaction of a fish faced to a high level of predation seems to disappear after two or three generations reared under artificial conditions; that is, after two generations, the common trout becomes non-sensitive to the predation risk; animals were active during the daylight and not during the night as their wild conspecifics [40]. As a consequence, domestication would decrease the level of defences against predators, as the reared animals would not experiment contacts with predators or some other life history traits should be affected by domestication and consequently affect the response of the animal to predator risk. For example, wild fishes react more rapidly to a predator than reared fishes [41, 42]. Wild animals may use natural refuges in their environment they know to escape from predation [43]. Moreover, wild individuals seem more careful to predators than reared fishes in the common carp (Cyprinus carpio); but these results are under suspicion because ‘wild’ animals are in fact reared individuals, which were returned back to natural conditions [44]. Domestication may also affect the reaction to a novel object in the environment; reared fishes approach more easily to a novel object and take more risks [36, 45]. This difference in behaviour is linked to physiological variations (heart activity, mobility, swimming abilities…) [35, 37]: but the results are not so clear and in a large number of cases, the responses of reared fishes to predators are variable [19, 46].
\nSome more recent results confirm the complexity of the relationships between this behavioural trait (anti-predator behaviour) and domestication. For example, the anti-predator behaviour of juvenile Atlantic salmon of conventional hatchery compared with that of wild-caught juveniles from the same population, tested in two unfamiliar environments, did not differ between the two strains in the spontaneous escape response [47], but after this first reaction, hatchery-reared juveniles stayed less time in association with the shelter than the wild animals. The same result has been found in the grass carp (Ctenopharyngodon idella); in the frame of restocking programs using hatchery-reared individuals, it is important to test the anti-predator behaviour. This behaviour was compared with that of wild-caught animals. The two groups exhibited a clear anti-predator behaviour; however, the hatchery-reared individuals showed lower aggregation and spent time in the risky areas and most of them were predated [48]. These variations between domesticated and wild strains in the display of the anti-predator behaviour are well documented in rainbow trout (Oncorhynchus mykiss). Comparisons between wild and hatchery population between clonal lines of rainbow trout derived from either wild and hatchery-reared populations identified several genes associated with behavioural variations between lines [49]. These genetic variations underlying anti-predator behaviours may be used in conservation programs for monitoring alleles of loci affecting predation in natural populations.
\nAs behaviour is a phenotype corresponding to the plasticity of the responses of animal to the set of environmental conditions, it is interesting to understand how development can affect the behaviour of different genotypes. Now, the existence of transgenic species offers a good tool to study this problem. By comparing wild-type siblings and transgenic individuals, Sundström et al. [50] found that wild and transgenic animals behave in the manner under natural like conditions; but until now, there are not a sufficient number of studies to conclude that genetically modified organisms are not affected by the complexity of natural conditions.
\nSocial behaviour is particularly developed in fishes, such as shoal [51], which is a part of the social life and is present in more than 25,000 species [52]. Shoal is important and ensures protection against a potential predator (a particular prey is undetectable in the group), but also it increases the foraging efficiency (the amount of food per individual is higher in groups than for solitary fishes whatever their diet). Shoal—defined as a group of individuals [51]—may be influenced by environmental factors, and domestication is one of these factors; reared conditions modify the fish environment. It limits the available space for fishes that could have for consequences a non-response of the fishes to environmental stimuli [53]; in reared conditions, food is distributed ad libitum, and such situation modifies the foraging behaviour limiting the exploration of the environment [54] and the predator avoidance [12, 32, 55]. In domesticated fishes, there is less variability of the age and size of the individuals, and so, the relations between fishes are modified and the results are counterbalanced; in some studies, they show that there is an increase of the aggressiveness between individuals [56, 57], and in other studies, they find that the aggressiveness is higher in domesticated populations [55, 58]. Growth in rearing situations is influenced by intra-specific competition [59, 60].
\nOne of the most important components of the social relations between individuals is the agonistic behaviour. Comparisons between wild and reared fishes show that new agonistic behaviours do not appear due to domestication [61]; agonistic behaviours are the same for both wild-reared individuals. In general, agonistic behaviours appear for the competition for resources: prediction is that agonistic behaviours must be less numerous when the quantity of resources increases. Domestication introduces the selection of individuals with a rapid growth; the consequences on the level of agonistic behaviours between individuals inside the groups are very dependent of the situation. Globally, it has been demonstrated that an effect on agonistic behaviours exists [62]. Agonistic behaviour can increase for domesticated fishes [58, 63, 64] or decrease [56] or be stable [57]. For example, the brown trout sea-ranched individuals have a higher growth rate and have no difference of activity with wild animals, but intensity of agonistic behaviours was higher in wild individuals [65]. These results could be interpreted as a consequence of the rearing conditions; in wild populations, agonistic behaviour has a function for space sharing, food accessibility [66], foraging efficiency and predator avoidance [67, 68]. So selection in rearing conditions leads to the individuals that have the most rapid growth but with particular behavioural traits (i.e. the most aggressive fishes); it is a known phenomenon, analysed as phenotypic selection (or economic selection by culturists) [69]. This implies that fishes are selected on their size and growth rate, and the dominance effect, which could be the result of competitive relationships, disappears if we introduce the size as variable [23]. But the dominance depends on the environment; this could be linked to the residence effect, which exists in wild fishes and not in reared ones [70]. In any case, competitive behaviours are the same; they vary in quality and intensity between wild and reared fishes [71]; for example, the high density for reared fishes in tanks could induce less territoriality and so a lower aggressiveness during dyadic confrontations [70, 72]. Competition and dominance have been tested in the salmon (Oncorhynchus tshawytscha) and the results showed that wild fishes were more aggressive than fishes from the first generation (F1) reared in aquaculture [73]. In general, the consequence of dominance is better growth rates for the dominant individuals whatever their origin (wild or reared). More recently, a relationship was found on the influence of domestication on brain size and aggressive behavioural changes. A study on rainbow trout lines highlighted that some behaviours such as ‘freeze’ and ‘escape’ are associated with a high level of domestication instead of ‘display’ and ‘yawn’ behaviours, which are linked to wild lines [74]. Moreover, these authors found that the total brain size and olfactory volume were associated with domestication.
\nAn important consequence of the level of aggressiveness between individuals is the existence of cannibalism [75]. It could appear either within the same cohort or between different cohorts. Cannibalism is a natural phenomenon, which is for regulating natural populations in many fish species. In cultured fishes, cannibalism has a negative effect on the populations; some individuals switch from food given by humans to the attacks and consumption of conspecifics.
\nThere is very few data on the influence of domestication or different lineages on the reproductive behaviour of fishes? This is the consequence that the reproductive behaviour in reared fishes received very little interest. It is the consequence that humans biased reproduction in reared fish populations; in fact, it is always handed by humans, and there is neither mate choice nor normal reproductive behavioural sequence. So, comparisons of reproductive behaviours between wild and reared fishes are based on behavioural differences between reared fishes that returned to natural environment and wild animals.
\nStudies focused on the choice of the spawn area; reared animals had more difficulties to find the good place to spawn with environmental features [76]. But the results are not so clear. Reared fishes may arrive earlier on the spawning zones than wild animals [77]. Fishes show different strategies with regard to their origin (wild or reared) [30, 78].
\nMost of the studies on the influence of domestication on the reproductive behaviour are done on salmonids because this is the group of species with the highest pressure for restocking the natural populations with hatchery-reared individuals, so it is absolutely necessary to evaluate their reproductive performances under natural environment. Coho Salmon (Oncorhynchus kisutch) produced by hatcheries have lower fitness in the wild than naturally produced salmon, but the factors underlying this difference remain an active area of research [71]. Neff et al. [79] used genetic parentage analysis of juveniles produced by experimentally mixed groups of wild and hatchery coho salmon to quantify male paternity. In all contexts, wild animals showed a higher paternity rate than hatchery-reared individuals.
\nThe concept of behavioural syndrome (synonyms = personality, temperament, behavioural differences) is defined as a collection of behavioural traits, which are constant over time and environmental situations [80]. It does not mean that these traits do not evolve with time for example, but that the combination of them is constant. This concept has been widely used in fishes. These behavioural syndromes may be dependent from the environmental situations (i.e. high or low density) and have different performances (i.e. boldness or shyness are the most efficient). This concept has been used for cultured fishes (Salmonidae) in order to select the most advantageous behavioural traits for the rearing of fishes in captivity. The human selection on economic criteria (size, growth) may be biased and this selection leads to keep the individuals that have the highest boldness (as in Salmonidae). But these results are not so clear, and in some cases, the selection of the individuals, which have the highest boldness, leads also to the selection of the most aggressive animals, i.e. salmon reared in farm for many generations are more aggressive and bold than individuals hatched in farm but from wild parents [72, 81]. Now, it is possible by comparing wild and domesticated strains, to show the existence of QTL for personality trait such as boldness. By testing the boldness of Zebra fish (Danio rerio), Wright et al. [54] showed that there are strong behavioural differences between a wild-derived strain of fish and a laboratory strain AB. Based on anti-predator behaviour, their results indicated a QTL for boldness on chromosomes 9 and 16 and suggest another genomic region that influences anti-predator behaviour on chromosome 21. So, these results confirm the possibility of QTL mapping of behavioural traits in zebra fish and the consequences of selection during domestication.
\nThese behavioural differences between captive of reared fish and their wild conspecifics could be used in the frame recovery programmes for threatened and endangered species. By comparing the boldness and prey acquisition behaviours of wild bull trout (Salvelinus confluentus) and reared ones, Brignon et al. [82] showed that wild fish and captive reared fish from complex habitats exhibited a greater level of boldness and prey acquisition ability, than fish reared in conventional captive environments. These results suggested that rearing fish in more complex captive environments could create a more wild-like phenotype than conventional rearing practices.
\nIn this frame of animal personality, or coping style, an important effect of the domestication is the reduction of emotional reactivity or responsiveness to a fear-evoking stimulus [83]; the emotional reactivity of wild fishes is better than those of reared individuals [84]. The emotional reactivity of an animal is necessary for provoking a flight response when there is a potential danger; it could be linked to a survival response. It seems that after domestication, fishes lost very rapidly, in only one generation, the stress response. This change in behaviour is probably directly linked to physiological changes: in the rainbow trout, two different lineages were selected on the basis of their rate of cortisol as responsiveness to stress. Individuals, which showed a low rate of cortisol, had a lower response to stress; they developed a better foraging behaviour but had a bad response to a potential danger. These individuals were well adapted to the environmental conditions of fish farms, but not the natural environment [85]. This is a general problem; the selection by humans of particular lineages of fishes based on their potentiality of growth and development has an influence on other life traits especially on behavioural traits. In the sea bass, the repetitive application of stress elements (pursuit of the fishes with a net, luminous changes, application of predator lure) modifies the foraging habits of wild fishes but also of reared ones. This could be interpreted as a habituation to the situation, which becomes less stressful [86].
\nIf the domestication process leads to a change in behavioural traits, empirical evidence for a difference in cognitive performance, however, is scarce. In the framework of animal personalities, differences in behaviour may arise during ontogeny through learning and bolder, and more aggressive animals (usually, the wild form) should learn faster. Such examples exist in vertebrates especially in mammals; by comparing wild cavies and domestic guinea pigs (Cavia porcellus) in behavioural tests. Domestic guinea pigs were less bold and aggressive than their wild congeners, but learnt an association faster [87]. Such studies exist also in fish but are scare, and now, results are not clearly established, leading an important field of research. For example, Klefoth et al. [88] tested two common genotypes of common carp, Cyprinus carpio L., differing in degree of domestication (a highly domesticated mirror carp and a less domesticated scaled carp) exposed to fishing. Domesticated mirror carp were more vulnerable to angling gear than scaled carp in both environments; these results were related to a bolder-foraging behaviour for the latter. Independently of genotype, fish become more difficult to catch, indicating learned hook avoidance, based on the boldness, so scaled carp get an advantage with a lower vulnerability to fishing. The study of Rodewald et al. [89] showed that after their release in natural environment, hatchery-reared salmon had a lower foraging rate than wild individuals. They showed that this difference was the consequence of higher abilities of learning the new environment and especially the presence of potential prey by the wild fish. Such studies should be initiated before the reintroduction of hatchery stock in the natural habitat, to ensure the success of the operation.
\n‘Domestication is that process by which a population of animals becomes adapted to man and to captive environment by genetic changes occurring over generation and environmentally-induced developmental events recurring in each generation [90]’. It affects all functions of the organisms and, in particular, behaviour. There are behavioural differences between wild and reared fishes (see Table 1), but these differences are more quantitative than qualitative; no new behaviours appear with domestication.
\nBehavioural traits | \nType of responses to environmental constraints | \nEffects of domestication | \nReferences | \n
---|---|---|---|
Swimming behaviour | \nResponse to a predator attack | \nWild is more reactive than hatchery | \n[10] | \n
Foraging behaviour | \nResponse to a novel environment | \nWild exhibits higher swimming abilities | \n[11, 12, 13, 14] | \n
Capacity to be released in the wild | \nWild exhibits lower dispersal | \n[16, 17, 18] | \n|
Changing the foraging strategy | \nHatchery exploits all the water column | \n[19, 20] | \n|
\n | Hatchery has a higher capture rate | \n[21, 22] | \n|
Predator avoidance | \nPrey profitability | \nWild has better rate than hatchery | \n[26] | \n
Consequences on survival | \nWild shows a higher rate of survival | \n[29] | \n|
Reaction in front of a predator | \nWild is more rapid | \n[41, 42] | \n|
\n | Reaction to a potential predator | \nHatchery takes more risks | \n[36, 45] | \n
Spontaneous escape responses | \nNo difference between wild and hatchery | \n[47] | \n|
Use of shelter | \nWild has a higher rate | \n[47, 48] | \n|
Social behaviour | \nAggressiveness | \nHatchery is more aggressive than wild | \n[56, 57] | \n
\n | It is higher for hatchery | \n[55, 58] | \n|
Agonistic behaviour | \nIt is higher for hatchery | \n[58, 63, 64] | \n|
\n | It is higher for wild | \n[56, 65] | \n|
\n | There is no difference | \n[57] | \n|
Competitive behaviour | \nTerritoriality is higher in wild | \n[70, 72] | \n|
\n | Dominance is higher in wild | \n[73] | \n|
Cannibalism | \nNo information? | \n\n | |
Reproduction | \nAbilities to reproduce after realising | \nPaternity rate higher for wild | \n[79] | \n
Choice of place to spawn | \n\n | \n | |
\n | Better for wild | \n[76] | \n|
\n | No difference | \n[77] | \n|
Personality | \nFitness in natural sites | \nHigher for wild | \n[71] | \n
Boldness | \nHigher for hatchery | \n[72, 80] | \n|
\n | Wild higher than for wild | \n[82] | \n|
QTL mapping | \nThere is no difference in prey acquisition | \n[54] | \n|
Stress | \nWild is more stressful than hatchery | \n[83, 84] | \n|
Cognitive abilities | \nLearning | \nWild is less bolder-foraging than hatchery | \n[88] | \n
Releasing | \nWild learns new environment faster | \n[89] | \n
Summary of the different results found for this review. The behavioural traits were divided in behavioural acts and their responses to domestication process in fish were briefly described. Wild referred to animals coming from wild strains and hatchery for fish larvae reared under farm or laboratory conditions.
The selection of individuals for economic reasons leads to the selection of fishes on morphological or developmental traits (growth, size). These traits are directly linked to other biological traits (i.e. behaviour) and their selection may lead to select fishes, which present some behaviours affecting the life in groups of high density and so the development of each individual (increase in aggressive and agonistic interactions between individuals, higher levels of cannibalism). One solution to prevent that is to identify as soon as possible in the fish development the behavioural profiles of the individuals under different domestication levels [91]. These studies lead to better knowledge of the fish larvae, which are difficult to test given their high sensitivity to environmental conditions.
\nIn this review, we saw that all behavioural traits may be impacted by domestication even after only one generation. For some traits, the results are clear and follow the same trend; the response to a predator is affected by domestication whatever the domesticated species and the reared environment. But in some cases, it is more difficult to find a common trend: foraging is affected but it depends on the type of food, and on the feeding conditions. It is the same for aggressiveness in the hatchery-reared individuals; it could decrease in that way we can put a high number of predators together if we give them a sufficient amount of food, but on the other hand, the high fish density in tank can produce a high level of aggressiveness between individuals leading to cannibalism event if the food is abundant. It is also true for other behavioural traits such as personality or cognitive capabilities; until now, there is a lack of studies on the influence of domestication on these behavioural traits and it is not possible to conclude. What we know is that the human selection on morphological or physiological traits of some individuals (even through a genetic program) has a direct influence on behavioural traits.
\nThis has two implications: first, it is necessary to study behavioural traits in the case of domestication of new species in order to determine the best environmental conditions of rearing, and second, these behavioural trait modifications must be into account when release of domesticated animals into natural habitats is considered. For these two points, we have to keep in mind that the consequences of behavioural selection traits through domestication correspond to the selection of a particular behavioural trait belonging to the natural behavioural range of the species under rearing environmental conditions; this might lead to a new species, the other behavioural traits of the species range disappearing. It is known under a genetic-environment process by which the epigenetic landscape is modified by the environment constraints influencing directly the genetic program [92, 93].
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