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Matrix population models

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946: 606: 650: 372: 941:{\displaystyle {\begin{aligned}{\begin{pmatrix}N_{t+l_{1}}\\N_{t+l_{2}}\\N_{t+l_{3}}\end{pmatrix}}&={\begin{pmatrix}F_{1}&F_{2}&F_{3}\\S_{1}&0&0\\0&S_{2}&0\end{pmatrix}}{\begin{pmatrix}N_{t_{1}}\\N_{t_{2}}\\N_{t_{3}}\end{pmatrix}}\end{aligned}}.} 615:
for this species. Each row in the first and third matrices corresponds to animals within a given age range (0–1 years, 1–2 years and 2–3 years). In a Leslie matrix the top row of the middle matrix consists of age-specific fertilities:
601:{\displaystyle {\begin{aligned}{\begin{pmatrix}N_{t+l_{i}}\\N_{t+l_{a}}\end{pmatrix}}&={\begin{pmatrix}S_{i}R_{i}&S_{a}R_{i}\\S_{i}&S_{a}\end{pmatrix}}{\begin{pmatrix}N_{t_{i}}\\N_{t_{a}}\end{pmatrix}}\end{aligned}}.} 321: 183:
Although BIDE models are conceptually simple, reliable estimates of the 5 variables contained therein (N, B, D, I and E) are often difficult to obtain. Usually a researcher attempts to estimate current abundance,
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For added simplicity it may help to think of time t as the end of the breeding season in year t and to imagine that one is studying a species that has only one discrete breeding season per year.
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of wildlife or human populations. Matrix algebra, in turn, is simply a form of algebraic shorthand for summarizing a larger number of often repetitious and tedious algebraic computations.
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can be constants or they can be functions of environment, such as habitat or population size. Randomness can also be incorporated into the environmental component.
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Caswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts.
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These models can give rise to interesting cyclical or seemingly chaotic patterns in abundance over time when fertility rates are high.
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technique. Estimates of B might be obtained via a ratio of immatures to adults soon after the breeding season, R
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Suppose that you are studying a species with a maximum lifespan of 4 years. The following is an age-based
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in the matrix above. Since this species does not live to be 4 years old the matrix does not contain an S
32: 197: 189: 28: 984: 20: 204:. Often, immigration and emigration are ignored because they are so difficult to estimate. 196:. Number of deaths can be obtained by estimating annual survival probability, usually via 362:= ratio of surviving young females at the end of the breeding season per breeding female 24: 1003: 612: 201: 180:
This equation is called a BIDE model (Birth, Immigration, Death, Emigration model).
39: 316:{\displaystyle N_{t+1}=N_{t,a}\times S_{a}+N_{t,i}\times R_{i}\times S_{i}} 159:
I = number of individuals immigrating into the population between N
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E = number of individuals emigrating from the population between N
355:= annual survival of immature females from time t to time t+1 348:= annual survival of adult females from time t to time t+1 861: 763: 663: 542: 458: 385: 653: 375: 219: 51: 149:
D = number of deaths within the population between N
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B = number of births within the population between N
366:In matrix notation this model can be expressed as: 940: 600: 315: 110: 200:methods, then multiplying present abundance and 995:Leslie Matrix Model demonstration (Silverlight) 8: 915: 910: 894: 889: 873: 868: 856: 837: 808: 794: 782: 770: 758: 735: 724: 708: 697: 681: 670: 658: 654: 652: 575: 570: 554: 549: 537: 523: 511: 497: 487: 475: 465: 453: 430: 419: 403: 392: 380: 376: 374: 307: 294: 275: 262: 243: 224: 218: 210:The BIDE model can then be expressed as: 75: 56: 50: 341:= number of immature females at time t 111:{\displaystyle N_{t+1}=N_{t}+B-D+I-E,} 7: 334:= number of adult females at time t 14: 972:Population dynamics of fisheries 27:. Population models are used in 1: 188:, often using some form of 1031: 17:Matrix population models 129:= abundance at time t+1 19:are a specific type of 942: 602: 317: 112: 943: 603: 318: 136:= abundance at time t 113: 651: 373: 217: 49: 1010:Population dynamics 938: 933: 925: 850: 745: 598: 593: 585: 531: 440: 313: 198:mark and recapture 190:mark and recapture 108: 29:population ecology 1015:Population models 1022: 947: 945: 944: 939: 934: 930: 929: 922: 921: 920: 919: 901: 900: 899: 898: 880: 879: 878: 877: 855: 854: 842: 841: 813: 812: 799: 798: 787: 786: 775: 774: 750: 749: 742: 741: 740: 739: 715: 714: 713: 712: 688: 687: 686: 685: 607: 605: 604: 599: 594: 590: 589: 582: 581: 580: 579: 561: 560: 559: 558: 536: 535: 528: 527: 516: 515: 502: 501: 492: 491: 480: 479: 470: 469: 445: 444: 437: 436: 435: 434: 410: 409: 408: 407: 322: 320: 319: 314: 312: 311: 299: 298: 286: 285: 267: 266: 254: 253: 235: 234: 117: 115: 114: 109: 80: 79: 67: 66: 21:population model 1030: 1029: 1025: 1024: 1023: 1021: 1020: 1019: 1000: 999: 980: 968: 961: 957: 932: 931: 924: 923: 911: 906: 903: 902: 890: 885: 882: 881: 869: 864: 857: 849: 848: 843: 833: 831: 825: 824: 819: 814: 804: 801: 800: 790: 788: 778: 776: 766: 759: 751: 744: 743: 731: 720: 717: 716: 704: 693: 690: 689: 677: 666: 659: 649: 648: 643: 639: 635: 631: 628:. Note, that F 627: 623: 619: 592: 591: 584: 583: 571: 566: 563: 562: 550: 545: 538: 530: 529: 519: 517: 507: 504: 503: 493: 483: 481: 471: 461: 454: 446: 439: 438: 426: 415: 412: 411: 399: 388: 381: 371: 370: 361: 354: 347: 340: 333: 303: 290: 271: 258: 239: 220: 215: 214: 195: 187: 176: 172: 166: 162: 156: 152: 146: 142: 135: 128: 71: 52: 47: 46: 42:can be modeled 12: 11: 5: 1028: 1026: 1018: 1017: 1012: 1002: 1001: 998: 997: 992: 979: 976: 975: 974: 967: 964: 959: 955: 949: 948: 937: 928: 918: 914: 909: 905: 904: 897: 893: 888: 884: 883: 876: 872: 867: 863: 862: 860: 853: 847: 844: 840: 836: 832: 830: 827: 826: 823: 820: 818: 815: 811: 807: 803: 802: 797: 793: 789: 785: 781: 777: 773: 769: 765: 764: 762: 757: 754: 752: 748: 738: 734: 730: 727: 723: 719: 718: 711: 707: 703: 700: 696: 692: 691: 684: 680: 676: 673: 669: 665: 664: 662: 657: 656: 641: 637: 633: 629: 625: 621: 617: 609: 608: 597: 588: 578: 574: 569: 565: 564: 557: 553: 548: 544: 543: 541: 534: 526: 522: 518: 514: 510: 506: 505: 500: 496: 490: 486: 482: 478: 474: 468: 464: 460: 459: 457: 452: 449: 447: 443: 433: 429: 425: 422: 418: 414: 413: 406: 402: 398: 395: 391: 387: 386: 384: 379: 378: 364: 363: 359: 356: 352: 349: 345: 342: 338: 335: 331: 324: 323: 310: 306: 302: 297: 293: 289: 284: 281: 278: 274: 270: 265: 261: 257: 252: 249: 246: 242: 238: 233: 230: 227: 223: 193: 185: 178: 177: 174: 170: 167: 164: 160: 157: 154: 150: 147: 144: 140: 137: 133: 130: 126: 119: 118: 107: 104: 101: 98: 95: 92: 89: 86: 83: 78: 74: 70: 65: 62: 59: 55: 25:matrix algebra 13: 10: 9: 6: 4: 3: 2: 1027: 1016: 1013: 1011: 1008: 1007: 1005: 996: 993: 990: 989:0-87893-096-5 986: 982: 981: 977: 973: 970: 969: 965: 963: 952: 935: 926: 916: 912: 907: 895: 891: 886: 874: 870: 865: 858: 851: 845: 838: 834: 828: 821: 816: 809: 805: 795: 791: 783: 779: 771: 767: 760: 755: 753: 746: 736: 732: 728: 725: 721: 709: 705: 701: 698: 694: 682: 678: 674: 671: 667: 660: 647: 646: 645: 614: 613:Leslie matrix 595: 586: 576: 572: 567: 555: 551: 546: 539: 532: 524: 520: 512: 508: 498: 494: 488: 484: 476: 472: 466: 462: 455: 450: 448: 441: 431: 427: 423: 420: 416: 404: 400: 396: 393: 389: 382: 369: 368: 367: 357: 350: 343: 336: 329: 328: 327: 308: 304: 300: 295: 291: 287: 282: 279: 276: 272: 268: 263: 259: 255: 250: 247: 244: 240: 236: 231: 228: 225: 221: 213: 212: 211: 208: 205: 203: 202:survival rate 199: 191: 181: 168: 158: 148: 138: 131: 124: 123: 122: 105: 102: 99: 96: 93: 90: 87: 84: 81: 76: 72: 68: 63: 60: 57: 53: 45: 44: 43: 41: 36: 34: 31:to model the 30: 26: 22: 18: 953: 950: 610: 365: 325: 209: 206: 182: 179: 120: 37: 16: 15: 954:The terms F 40:populations 1004:Categories 978:References 23:that uses 301:× 288:× 256:× 100:− 88:− 966:See also 33:dynamics 636:×R 326:where: 121:where: 987:  644:term. 958:and S 624:and F 173:and N 163:and N 153:and N 143:and N 985:ISBN 38:All 632:= S 620:, F 339:t,i 332:t,a 175:t+1 165:t+1 155:t+1 145:t+1 127:t+1 1006:: 991:. 960:i 956:i 936:. 927:) 917:3 913:t 908:N 896:2 892:t 887:N 875:1 871:t 866:N 859:( 852:) 846:0 839:2 835:S 829:0 822:0 817:0 810:1 806:S 796:3 792:F 784:2 780:F 772:1 768:F 761:( 756:= 747:) 737:3 733:l 729:+ 726:t 722:N 710:2 706:l 702:+ 699:t 695:N 683:1 679:l 675:+ 672:t 668:N 661:( 642:3 638:i 634:i 630:1 626:3 622:2 618:1 616:F 596:. 587:) 577:a 573:t 568:N 556:i 552:t 547:N 540:( 533:) 525:a 521:S 513:i 509:S 499:i 495:R 489:a 485:S 477:i 473:R 467:i 463:S 456:( 451:= 442:) 432:a 428:l 424:+ 421:t 417:N 405:i 401:l 397:+ 394:t 390:N 383:( 360:i 358:R 353:i 351:S 346:a 344:S 337:N 330:N 309:i 305:S 296:i 292:R 283:i 280:, 277:t 273:N 269:+ 264:a 260:S 251:a 248:, 245:t 241:N 237:= 232:1 229:+ 226:t 222:N 194:i 186:t 184:N 171:t 161:t 151:t 141:t 134:t 132:N 125:N 106:, 103:E 97:I 94:+ 91:D 85:B 82:+ 77:t 73:N 69:= 64:1 61:+ 58:t 54:N

Index

population model
matrix algebra
population ecology
dynamics
populations
mark and recapture
mark and recapture
survival rate
Leslie matrix
Population dynamics of fisheries
ISBN
0-87893-096-5
Leslie Matrix Model demonstration (Silverlight)
Categories
Population dynamics
Population models

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