Using chaseR
By Shaun Killen
For fishes, one way of estimating maximum metabolic rates (MMR) is to measure rates of oxygen uptake immediately following exhaustive exercise. This method is often referred to as a “chase” protocol, because the animals are often made to swim with gentle hand movements or some other form of mechanical stimulation. After exhaustion, they are transferred to a respirometry chamber and the decline in water oxygen content is then measured over time. The slope of this decline can be used to estimate the animal’s oxygen uptake as a proxy for aerobic metabolism.
Because the animal will be recovering throughout this period, researchers determine the period after exercise with the steepest decline in water oxygen content, then to use this slope for calculating MMR. Shorter time frames tend to yield steeper slopes, because animals recover over time and so longer durations can be non-linear or show increasingly shallow slopes. However, shorter durations can also introduce more noise and so be associated with poorer model fits and lower r-squared values.
This R package, chaseR, will calculate all slopes throughout a dataset of water oxygen content through time, using a rolling regression of a duration specified by the user. The user can also quickly trial many different window size to see which rolling window duration offers the best compromise between a steep slope and a good model fit. Finally, the maximum metabolic rate after exhaustion is calculated, as well as a range of other variables of interest including the rate of recovery after exercise and the integrated excess post-exercise oxygen consumption (EPOC).
This document introduces you to chaseR’s functions, and shows you a typical workflow to return several plots and values of interest, including an estimate of MMR.
Data
To explore a basic workflow we’ll use an example dataset that contains six columns:
- time (in seconds);
- water oxygen concentration for an empty (blank) respirometry chamber (MO2_blank; in mg O2/L); then (3-6) up to four columns that contain data of water oxygen content for chambers that contained fish (MO2_fish1, MO2_fish2, MO2_fish3, and MO2_fish4; in mg O2/L). These four columns correspond to data that would be collected on a four-channel oxygen meter (e.g. PyroScience Firesting) for fish in four separate respirometry chambers .
It is imperative that the data you input into chaseR be in this format!
data <- read.csv(system.file("extdata/test_data.csv", package="chaseR"))
# Display the first few rows of the data
head(data)
#> time MO2_blank MO2_fish1 MO2_fish2 MO2_fish3 MO2_fish4
#> 1 0 11.000 10.676 10.962 10.785 10.862
#> 2 2 11.003 10.680 10.962 10.788 10.857
#> 3 4 11.000 10.681 10.960 10.796 10.853
#> 4 6 10.998 10.686 10.962 10.808 10.852
#> 5 8 10.994 10.699 10.958 10.816 10.846
#> 6 10 10.992 10.715 10.953 10.818 10.839In preparing this data file, first populate the “time” column by starting with 0, then increasing each row by intervals representing how frequently the water oxygen content was measured (e.g. every two seconds).
For the fish data, load your oxygen meter output file into Excel, then copy and paste the data for each fish, starting from when they were first placed in their respective respirometers. Begin copying the data from the row where they first entered the respirometer. If you don’t have this time noted down somewhere, you can also plot the data in Excel and visually examine when the O2 content started to decrease. Hover your cursor over this point, and you will be shown a time for that data point. This will likely be close to the entry time. Once you have selected the appropriate data for a given fish, paste it into your separate file with the appropriate headings (e.g. MO2_fish1, etc.), aligning the first row with time=0 in the “time” column.
You will also need to do this for the oxygen concentration data for an empty chamber, whether this was run in parallel, or before or after your measurements for MMR. Paste this data into the MO2_blank column, again aligning the first row (when the blank started) with time = 0.
Analysing Window Sizes
A good place to start the analysis is to estimate what rolling window duration is most appropriate for calculating your slopes. For this we can use the window_effect function.
As mentioned above, smaller rolling windows tend to yield steeper (more negative) slopes, but also have lower r-squared values due to a decreased signal-to-noise ratio. The function window_effect allows you to specify a range of window durations to test, then see a plot of minimum slope (the steepest negative slope in the dataset) vs window duration. Points are also colour-scaled by r-squared value.
Finally, the function will return the minimum window duration needed to give a mean r-squared (across all slopes) above a specific threshold that is specified by the user (e.g. r^2 = 0.95).
This function contains several arguments:
data: this is the dataframe to be used. exclude_time: This is any time (in seconds) you want excluded from the beginning of the data. For example, it is common to exclude the first 30 seconds after transferring the fish to the respirometer, to ensure that mixing has occurred and that any steep slopes aren’t artefacts. window_durations: This specifies the window durations you want to test (in seconds). The first value is the first duration to be tested, the second value is the last duration to be tested, and the third value is the increments that will be tested in between.In the example below, window durations from 30 to 600 seconds will be tested at every 30 second interval. r_squared_threshold: This is the minimum value for r^2 that you want to consider for usable slope estimates.
window_effect(data, exclude_time = 30, window_durations = seq(30, 600, 30), r_squared_threshold = 0.90)
#> Analyzing window duration: 30 seconds
#> Analyzing window duration: 60 seconds
#> Analyzing window duration: 90 seconds
#> Analyzing window duration: 120 seconds
#> Analyzing window duration: 150 seconds
#> Analyzing window duration: 180 seconds
#> Analyzing window duration: 210 seconds
#> Analyzing window duration: 240 seconds
#> Analyzing window duration: 270 seconds
#> Analyzing window duration: 300 seconds
#> Analyzing window duration: 330 seconds
#> Analyzing window duration: 360 seconds
#> Analyzing window duration: 390 seconds
#> Analyzing window duration: 420 seconds
#> Analyzing window duration: 450 seconds
#> Analyzing window duration: 480 seconds
#> Analyzing window duration: 510 seconds
#> Analyzing window duration: 540 seconds
#> Analyzing window duration: 570 seconds
#> Analyzing window duration: 600 seconds
#>
#> Minimum window durations for R-squared threshold of 0.9 :
#> $MO2_fish1
#> [1] 150
#>
#> $MO2_fish2
#> [1] 90
#>
#> $MO2_fish3
#> [1] 120
#>
#> $MO2_fish4
#> [1] 420The plots show us the general trends that we were expecting - that the minimum estimated slope increases with window size and that model r-squared values are higher. In addition to these plots, we also get a text output showing the minimum window size for each fish that is required to achieve the threshold r-squared value that we specified. As you can see, there is some variation among fish for this value.
For calculating our slopes (see the next step in the workflow), we may consider using different window sizes for each individual fish, or we could use a single window size for all fish that ensures our minimal r-squared threshold is being met. It is probably easiest to select a common window size. This will make our analyses more straightforward an will also avoid any biases associated with using different window sizes for different individuals. However, if there are any individuals that require unusually large window sizes relative to the others, it may be worth examining the data for these fish and considered whether a different window size should be used, or indeed if their data can be used at all if their mean r-squared values are too low. In the example above, for example, a window duration of 150 seconds would suffice for individuals 1-3, but fish 4 may need to be examined more carefully.
Overall, we may wish to process all fish in the dataset through this window size analysis to determine the optimal window size to apply to the entire dataset, before calculating our slopes.
Calculating Slopes
Now we can calculate our slopes using our rolling window. The “slopes” function will do this for us, after adjusting for the slope of background microbial respiration, as well as return some other useful measures. This function has the following arguments:
data: this is the dataframe to be used. exclude_time: this is any time (in seconds) you want excluded from the beginning of the data. As noted above, it is common to exclude the first 30 seconds after transferring the fish to the respirometer, to ensure that mixing has occurred and that any steep slopes aren’t artefacts. window_duration: The rolling window duration that will be used to calculate your slopes (in seconds), ideally determined using the window_effect function.
slopes(data, exclude_time = 30, window_duration = 150)
#> $MO2_fish1
#> $MO2_fish1$`Adjusted Slopes`
#> [1] -0.0020682171 -0.0020679468 -0.0020726410 -0.0020847889 -0.0021004930
#> [6] -0.0021212470 -0.0021459554 -0.0021762114 -0.0022061687 -0.0022379895
#> [11] -0.0022695542 -0.0023025414 -0.0023348885 -0.0023681744 -0.0023996253
#> [16] -0.0024313039 -0.0024653579 -0.0025033664 -0.0025447178 -0.0025866381
#> [21] -0.0026306211 -0.0026760122 -0.0027210763 -0.0027651730 -0.0028076908
#> [26] -0.0028486012 -0.0028889283 -0.0029280748 -0.0029665243 -0.0030010763
#> [31] -0.0030317590 -0.0030591844 -0.0030839639 -0.0031018871 -0.0031140635
#> [36] -0.0031194404 -0.0031234091 -0.0031228259 -0.0031172214 -0.0031077477
#> [41] -0.0031004361 -0.0031003081 -0.0031060407 -0.0031163110 -0.0031249169
#> [46] -0.0031319297 -0.0031325272 -0.0031266524 -0.0031131246 -0.0030955428
#> [51] -0.0030748885 -0.0030505926 -0.0030252299 -0.0030002086 -0.0029784874
#> [56] -0.0029576908 -0.0029354290 -0.0029120151 -0.0028915599 -0.0028739923
#> [61] -0.0028618871 -0.0028497960 -0.0028403223 -0.0028297107 -0.0028163821
#> [66] -0.0028035798 -0.0027881744 -0.0027707491 -0.0027470791 -0.0027203935
#> [71] -0.0026922854 -0.0026649027 -0.0026383309 -0.0026105499 -0.0025827121
#> [76] -0.0025517022 -0.0025240208 -0.0025008629 -0.0024850592 -0.0024735514
#> [81] -0.0024659127 -0.0024627121 -0.0024634376 -0.0024675058 -0.0024727121
#> [86] -0.0024774489 -0.0024800805 -0.0024826979 -0.0024879468 -0.0024942626
#> [91] -0.0025006068 -0.0025085727 -0.0025208487 -0.0025385443 -0.0025585158
#> [96] -0.0025819013 -0.0026103650 -0.0026439354 -0.0026783736 -0.0027137221
#> [101] -0.0027473352 -0.0027835087 -0.0028222427 -0.0028629966 -0.0029025272
#> [106] -0.0029407633 -0.0029739070 -0.0030056424 -0.0030289994 -0.0030513750
#> [111] -0.0030694689 -0.0030848174 -0.0030952583 -0.0030986296 -0.0030991275
#> [116] -0.0030955855 -0.0030906353 -0.0030784020 -0.0030625983 -0.0030415457
#> [121] -0.0030183878 -0.0029904219 -0.0029586723 -0.0029241773 -0.0028885300
#> [126] -0.0028501374 -0.0028084589 -0.0027641061 -0.0027225699 -0.0026856282
#> [131] -0.0026555571 -0.0026297960 -0.0026057989 -0.0025868800 -0.0025709767
#> [136] -0.0025650876 -0.0025618017 -0.0025648743 -0.0025694831 -0.0025739781
#> [141] -0.0025783309 -0.0025798529 -0.0025801232 -0.0025748458 -0.0025672071
#> [146] -0.0025577761 -0.0025471360 -0.0025363394 -0.0025237221 -0.0025113608
#> [151] -0.0024965101 -0.0024854717 -0.0024776054 -0.0024749312 -0.0024731104
#> [156] -0.0024721147 -0.0024735656 -0.0024758985 -0.0024813323 -0.0024820151
#> [161] -0.0024801943 -0.0024748885 -0.0024693693 -0.0024610763 -0.0024506353
#> [166] -0.0024349454 -0.0024162967 -0.0023937932 -0.0023697534 -0.0023411332
#> [171] -0.0023096680 -0.0022781460 -0.0022467093 -0.0022117590 -0.0021744618
#> [176] -0.0021333095 -0.0020910620 -0.0020499667 -0.0020074774 -0.0019663679
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#> [201] -0.0011608344 -0.0011545755 -0.0011502655 -0.0011509198 -0.0011542768
#> [206] -0.0011634802 -0.0011758273 -0.0011880748 -0.0012007633 -0.0012160549
#> [211] -0.0012371787 -0.0012620151 -0.0012900663 -0.0013229824 -0.0013575770
#> [216] -0.0013938643 -0.0014281317 -0.0014614888 -0.0014923565 -0.0015223280
#> [221] -0.0015503223 -0.0015758131 -0.0016041630 -0.0016343195 -0.0016689425
#> [226] -0.0017048458 -0.0017463679 -0.0017930820 -0.0018467093 -0.0019014888
#> [231] -0.0019606637 -0.0020199952 -0.0020842911 -0.0021476623 -0.0022079184
#> [236] -0.0022660407 -0.0023198672 -0.0023725983 -0.0024194262 -0.0024651303
#> [241] -0.0025048458 -0.0025400805 -0.0025686154 -0.0025924987 -0.0026100521
#> [246] -0.0026206922 -0.0026270791 -0.0026291844 -0.0026295684 -0.0026259838
#> [251] -0.0026183451 -0.0026076339 -0.0025958985 -0.0025803792 -0.0025626125
#> [256] -0.0025388003 -0.0025099810 -0.0024778046 -0.0024391417 -0.0023982882
#> [261] -0.0023504646 -0.0022989425 -0.0022411189 -0.0021791701 -0.0021142057
#> [266] -0.0020452441 -0.0019723992 -0.0018962967 -0.0018181033 -0.0017411332
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#> [346] -0.0009239923 -0.0009416737 -0.0009574632 -0.0009723849 -0.0009863963
#> [351] -0.0010010478 -0.0010168373 -0.0010284305 -0.0010379468 -0.0010432526
#> [356] -0.0010486154 -0.0010524276 -0.0010552157 -0.0010585727 -0.0010630393
#> [361] -0.0010686154 -0.0010747462 -0.0010792697 -0.0010832242 -0.0010881886
#> [366] -0.0010946751 -0.0011020862 -0.0011082171 -0.0011146324 -0.0011197534
#> [371] -0.0011220151 -0.0011209056 -0.0011190706 -0.0011186012 -0.0011178757
#> [376] -0.0011168515 -0.0011155002 -0.0011132953 -0.0011092128 -0.0011027406
#> [381] -0.0010970649 -0.0010942911 -0.0010922569 -0.0010898814 -0.0010871502
#> [386] -0.0010856566 -0.0010832384 -0.0010809056 -0.0010764532 -0.0010751588
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#> [481] -0.0012932384 -0.0012829539 -0.0012751872 -0.0012662398 -0.0012556140
#> [486] -0.0012433664 -0.0012306068 -0.0012189710 -0.0012100521 -0.0012022427
#> [491] -0.0011976623 -0.0011930962 -0.0011912185 -0.0011898814 -0.0011912043
#> [496] -0.0011908913 -0.0011889283 -0.0011847605 -0.0011810336 -0.0011771929
#> [501] -0.0011742626 -0.0011716453 -0.0011724703 -0.0011745755 -0.0011774489
#> [506] -0.0011800379 -0.0011828544 -0.0011869511 -0.0011918159 -0.0011990990
#> [511] -0.0012061829 -0.0012115172 -0.0012135371 -0.0012154717 -0.0012163110
#> [516] -0.0012155997 -0.0012128828 -0.0012077192 -0.0012023280 -0.0011962398
#> [521] -0.0011915884 -0.0011851445 -0.0011758416 -0.0011642484 -0.0011515030
#> [526] -0.0011371502 -0.0011217875 -0.0011059554 -0.0010896253 -0.0010754575
#> [531] -0.0010613608 -0.0010490137 -0.0010352868 -0.0010202370 -0.0010028259
#> [536] -0.0009867946 -0.0009689283 -0.0009529255 -0.0009350023 -0.0009199241
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#> [711] -0.0011385443 -0.0011429255 -0.0011455428 -0.0011490279 -0.0011512185
#> [716] -0.0011531673 -0.0011517022 -0.0011495258 -0.0011461260 -0.0011420151
#> [721] -0.0011392555 -0.0011393835 -0.0011412896 -0.0011433522 -0.0011455571
#> [726] -0.0011478899 -0.0011524845 -0.0011577192 -0.0011678330 -0.0011796111
#> [731] -0.0011946324 -0.0012101943 -0.0012246324 -0.0012389425 -0.0012530535
#> [736] -0.0012695684 -0.0012868657 -0.0013022854 -0.0013190137 -0.0013322000
#> [741] -0.0013439212 -0.0013546609 -0.0013638359 -0.0013746040 -0.0013858558
#> [746] -0.0013996822 -0.0014118017 -0.0014222142 -0.0014309340 -0.0014369226
#> [751] -0.0014402086 -0.0014408344 -0.0014409625 -0.0014384874 -0.0014340066
#> [756] -0.0014276054 -0.0014188430 -0.0014088572 -0.0013971929 -0.0013865955
#> [761] -0.0013739212 -0.0013608487 -0.0013469084 -0.0013337363 -0.0013218728
#> [766] -0.0013097249 -0.0012978330 -0.0012862114 -0.0012738216 -0.0012617590
#> [771] -0.0012489852 -0.0012381602 -0.0012271502 -0.0012143622 -0.0012008629
#> [776] -0.0011871929 -0.0011738928 -0.0011609767 -0.0011473921 -0.0011331531
#> [781] -0.0011182740 -0.0011016737 -0.0010843764 -0.0010663536 -0.0010476054
#> [786] -0.0010276054 -0.0010063536 -0.0009849169 -0.0009654290 -0.0009457562
#> [791] -0.0009248458 -0.0009000236 -0.0008739354 -0.0008455144 -0.0008163963
#> [796] -0.0007903650 -0.0007679895 -0.0007492839 -0.0007305215 -0.0007122569
#> [801] -0.0006950307 -0.0006788572 -0.0006643053 -0.0006519724 -0.0006408629
#> [806] -0.0006331531 -0.0006272783 -0.0006237932 -0.0006200805 -0.0006178188
#> [811] -0.0006176196 -0.0006200521 -0.0006219013 -0.0006253295 -0.0006277192
#> [816] -0.0006317590 -0.0006395684 -0.0006489994 -0.0006611474 -0.0006718444
#> [821] -0.0006844191 -0.0006973209 -0.0007110763 -0.0007251303 -0.0007389141
#> [826] -0.0007529113 -0.0007638928 -0.0007755997 -0.0007874632 -0.0007999241
#> [831] -0.0008091844 -0.0008157420 -0.0008206353 -0.0008227690 -0.0008247889
#> [836] -0.0008240208 -0.0008220293 -0.0008187861 -0.0008164105 -0.0008154433
#> [841] -0.0008142911 -0.0008129682 -0.0008109625 -0.0008088003 -0.0008059411
#> [846] -0.0008039639 -0.0008055144 -0.0008100094 -0.0008173494 -0.0008274347
#> [851] -0.0008412327 -0.0008586438 -0.0008779895 -0.0008970933 -0.0009159269
#> [856] -0.0009344618 -0.0009558416 -0.0009789425 -0.0010021431 -0.0010243622
#> [861] -0.0010450592 -0.0010642342 -0.0010776908 -0.0010892839 -0.0010975058
#> [866] -0.0011050450 -0.0011123849 -0.0011194831 -0.0011231389 -0.0011065243
#> [871] -0.0010506922 -0.0009346609 -0.0007512327 -0.0005007775 -0.0001889141
#> [876] 0.0001798626 0.0006032338
#>
#> $MO2_fish1$`Starting Times`
#> [1] 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58
#> [16] 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88
#> [31] 90 92 94 96 98 100 102 104 106 108 110 112 114 116 118
#> [46] 120 122 124 126 128 130 132 134 136 138 140 142 144 146 148
#> [61] 150 152 154 156 158 160 162 164 166 168 170 172 174 176 178
#> [76] 180 182 184 186 188 190 192 194 196 198 200 202 204 206 208
#> [91] 210 212 214 216 218 220 222 224 226 228 230 232 234 236 238
#> [106] 240 242 244 246 248 250 252 254 256 258 260 262 264 266 268
#> [121] 270 272 274 276 278 280 282 284 286 288 290 292 294 296 298
#> [136] 300 302 304 306 308 310 312 314 316 318 320 322 324 326 328
#> [151] 330 332 334 336 338 340 342 344 346 348 350 352 354 356 358
#> [166] 360 362 364 366 368 370 372 374 376 378 380 382 384 386 388
#> [181] 390 392 394 396 398 400 402 404 406 408 410 412 414 416 418
#> [196] 420 422 424 426 428 430 432 434 436 438 440 442 444 446 448
#> [211] 450 452 454 456 458 460 462 464 466 468 470 472 474 476 478
#> [226] 480 482 484 486 488 490 492 494 496 498 500 502 504 506 508
#> [241] 510 512 514 516 518 520 522 524 526 528 530 532 534 536 538
#> [256] 540 542 544 546 548 550 552 554 556 558 560 562 564 566 568
#> [271] 570 572 574 576 578 580 582 584 586 588 590 592 594 596 598
#> [286] 600 602 604 606 608 610 612 614 616 618 620 622 624 626 628
#> [301] 630 632 634 636 638 640 642 644 646 648 650 652 654 656 658
#> [316] 660 662 664 666 668 670 672 674 676 678 680 682 684 686 688
#> [331] 690 692 694 696 698 700 702 704 706 708 710 712 714 716 718
#> [346] 720 722 724 726 728 730 732 734 736 738 740 742 744 746 748
#> [361] 750 752 754 756 758 760 762 764 766 768 770 772 774 776 778
#> [376] 780 782 784 786 788 790 792 794 796 798 800 802 804 806 808
#> [391] 810 812 814 816 818 820 822 824 826 828 830 832 834 836 838
#> [406] 840 842 844 846 848 850 852 854 856 858 860 862 864 866 868
#> [421] 870 872 874 876 878 880 882 884 886 888 890 892 894 896 898
#> [436] 900 902 904 906 908 910 912 914 916 918 920 922 924 926 928
#> [451] 930 932 934 936 938 940 942 944 946 948 950 952 954 956 958
#> [466] 960 962 964 966 968 970 972 974 976 978 980 982 984 986 988
#> [481] 990 992 994 996 998 1000 1002 1004 1006 1008 1010 1012 1014 1016 1018
#> [496] 1020 1022 1024 1026 1028 1030 1032 1034 1036 1038 1040 1042 1044 1046 1048
#> [511] 1050 1052 1054 1056 1058 1060 1062 1064 1066 1068 1070 1072 1074 1076 1078
#> [526] 1080 1082 1084 1086 1088 1090 1092 1094 1096 1098 1100 1102 1104 1106 1108
#> [541] 1110 1112 1114 1116 1118 1120 1122 1124 1126 1128 1130 1132 1134 1136 1138
#> [556] 1140 1142 1144 1146 1148 1150 1152 1154 1156 1158 1160 1162 1164 1166 1168
#> [571] 1170 1172 1174 1176 1178 1180 1182 1184 1186 1188 1190 1192 1194 1196 1198
#> [586] 1200 1202 1204 1206 1208 1210 1212 1214 1216 1218 1220 1222 1224 1226 1228
#> [601] 1230 1232 1234 1236 1238 1240 1242 1244 1246 1248 1250 1252 1254 1256 1258
#> [616] 1260 1262 1264 1266 1268 1270 1272 1274 1276 1278 1280 1282 1284 1286 1288
#> [631] 1290 1292 1294 1296 1298 1300 1302 1304 1306 1308 1310 1312 1314 1316 1318
#> [646] 1320 1322 1324 1326 1328 1330 1332 1334 1336 1338 1340 1342 1344 1346 1348
#> [661] 1350 1352 1354 1356 1358 1360 1362 1364 1366 1368 1370 1372 1374 1376 1378
#> [676] 1380 1382 1384 1386 1388 1390 1392 1394 1396 1398 1400 1402 1404 1406 1408
#> [691] 1410 1412 1414 1416 1418 1420 1422 1424 1426 1428 1430 1432 1434 1436 1438
#> [706] 1440 1442 1444 1446 1448 1450 1452 1454 1456 1458 1460 1462 1464 1466 1468
#> [721] 1470 1472 1474 1476 1478 1480 1482 1484 1486 1488 1490 1492 1494 1496 1498
#> [736] 1500 1502 1504 1506 1508 1510 1512 1514 1516 1518 1520 1522 1524 1526 1528
#> [751] 1530 1532 1534 1536 1538 1540 1542 1544 1546 1548 1550 1552 1554 1556 1558
#> [766] 1560 1562 1564 1566 1568 1570 1572 1574 1576 1578 1580 1582 1584 1586 1588
#> [781] 1590 1592 1594 1596 1598 1600 1602 1604 1606 1608 1610 1612 1614 1616 1618
#> [796] 1620 1622 1624 1626 1628 1630 1632 1634 1636 1638 1640 1642 1644 1646 1648
#> [811] 1650 1652 1654 1656 1658 1660 1662 1664 1666 1668 1670 1672 1674 1676 1678
#> [826] 1680 1682 1684 1686 1688 1690 1692 1694 1696 1698 1700 1702 1704 1706 1708
#> [841] 1710 1712 1714 1716 1718 1720 1722 1724 1726 1728 1730 1732 1734 1736 1738
#> [856] 1740 1742 1744 1746 1748 1750 1752 1754 1756 1758 1760 1762 1764 1766 1768
#> [871] 1770 1772 1774 1776 1778 1780 1782
#>
#> $MO2_fish1$`Minimum Adjusted Slope`
#> [1] -0.003132527
#>
#> $MO2_fish1$`Percentage of Fish Slope (%)`
#> [1] 1.234847
#>
#> $MO2_fish1$`Start Time for Minimum Slope`
#> [1] 122
#>
#> $MO2_fish1$`R-Squared for Minimum Slope`
#> [1] 0.952261
#>
#> $MO2_fish1$`Mean R-Squared for all windows`
#> [1] 0.9147206
#>
#>
#> $MO2_fish2
#> $MO2_fish2$`Adjusted Slopes`
#> [1] -0.0027504077 -0.0028486865 -0.0029429682 -0.0030348316 -0.0031221004
#> [6] -0.0032041915 -0.0032810336 -0.0033541915 -0.0034247462 -0.0034916737
#> [11] -0.0035550592 -0.0036181886 -0.0036821716 -0.0037448743 -0.0038020578
#> [16] -0.0038564248 -0.0039058700 -0.0039530962 -0.0039981317 -0.0040415030
#> [21] -0.0040810478 -0.0041140919 -0.0041422427 -0.0041671218 -0.0041898103
#> [26] -0.0042076339 -0.0042205215 -0.0042267377 -0.0042288572 -0.0042268373
#> [31] -0.0042195827 -0.0042054433 -0.0041838074 -0.0041551303 -0.0041219724
#> [36] -0.0040820862 -0.0040332100 -0.0039714461 -0.0039003366 -0.0038186154
#> [41] -0.0037282882 -0.0036293124 -0.0035275627 -0.0034235940 -0.0033179895
#> [46] -0.0032129824 -0.0031118871 -0.0030158842 -0.0029213608 -0.0028305784
#> [51] -0.0027425841 -0.0026591132 -0.0025787007 -0.0025014603 -0.0024285727
#> [56] -0.0023585585 -0.0022958558 -0.0022363963 -0.0021829966 -0.0021336083
#> [61] -0.0020898956 -0.0020503935 -0.0020131531 -0.0019825983 -0.0019604646
#> [66] -0.0019468657 -0.0019403081 -0.0019371360 -0.0019390137 -0.0019486581
#> [71] -0.0019650023 -0.0019869938 -0.0020125272 -0.0020426979 -0.0020769938
#> [76] -0.0021137932 -0.0021541488 -0.0021958842 -0.0022378899 -0.0022806780
#> [81] -0.0023231389 -0.0023694689 -0.0024163679 -0.0024648174 -0.0025120578
#> [86] -0.0025590279 -0.0026061545 -0.0026571076 -0.0027091701 -0.0027601090
#> [91] -0.0028098245 -0.0028571787 -0.0029021147 -0.0029440919 -0.0029857847
#> [96] -0.0030271645 -0.0030660122 -0.0031011901 -0.0031326694 -0.0031609909
#> [101] -0.0031851019 -0.0032087434 -0.0032319155 -0.0032540635 -0.0032735798
#> [106] -0.0032909767 -0.0033056851 -0.0033182029 -0.0033269084 -0.0033350307
#> [111] -0.0033404646 -0.0033442768 -0.0033453579 -0.0033431815 -0.0033378046
#> [116] -0.0033281744 -0.0033153152 -0.0032991986 -0.0032803935 -0.0032552299
#> [121] -0.0032264390 -0.0031951588 -0.0031630820 -0.0031281317 -0.0030919440
#> [126] -0.0030566666 -0.0030196396 -0.0029814319 -0.0029404930 -0.0029011189
#> [131] -0.0028601090 -0.0028190706 -0.0027758558 -0.0027331389 -0.0026899098
#> [136] -0.0026457278 -0.0025985158 -0.0025498387 -0.0025018017 -0.0024560265
#> [141] -0.0024125556 -0.0023708913 -0.0023294546 -0.0022882740 -0.0022479753
#> [146] -0.0022081886 -0.0021685158 -0.0021285158 -0.0020888003 -0.0020499810
#> [151] -0.0020126694 -0.0019737647 -0.0019360549 -0.0018964248 -0.0018586581
#> [156] -0.0018206637 -0.0017873209 -0.0017560549 -0.0017274774 -0.0017026979
#> [161] -0.0016807064 -0.0016621004 -0.0016474774 -0.0016358416 -0.0016283167
#> [166] -0.0016222854 -0.0016183167 -0.0016148316 -0.0016086581 -0.0016025130
#> [171] -0.0015970080 -0.0015938216 -0.0015892697 -0.0015839212 -0.0015767235
#> [176] -0.0015703223 -0.0015668088 -0.0015672214 -0.0015710051 -0.0015759980
#> [181] -0.0015858842 -0.0015974205 -0.0016116595 -0.0016253864 -0.0016407064
#> [186] -0.0016559980 -0.0016707491 -0.0016876623 -0.0017019297 -0.0017167093
#> [191] -0.0017293124 -0.0017434945 -0.0017544902 -0.0017649738 -0.0017722142
#> [196] -0.0017782740 -0.0017815172 -0.0017824703 -0.0017843053 -0.0017848600
#> [201] -0.0017889425 -0.0017912327 -0.0017943906 -0.0017946609 -0.0017963394
#> [206] -0.0017957562 -0.0017966808 -0.0017991132 -0.0018025130 -0.0018047462
#> [211] -0.0018057847 -0.0018071787 -0.0018110478 -0.0018163394 -0.0018230820
#> [216] -0.0018318302 -0.0018415457 -0.0018527832 -0.0018639497 -0.0018744902
#> [221] -0.0018816879 -0.0018881744 -0.0018923280 -0.0018946893 -0.0018931673
#> [226] -0.0018883736 -0.0018782171 -0.0018637932 -0.0018425130 -0.0018161118
#> [231] -0.0017852583 -0.0017516595 -0.0017169938 -0.0016813039 -0.0016467946
#> [236] -0.0016103366 -0.0015725556 -0.0015351303 -0.0014991559 -0.0014662398
#> [241] -0.0014353010 -0.0014057989 -0.0013798387 -0.0013536083 -0.0013291559
#> [246] -0.0013058558 -0.0012862825 -0.0012671502 -0.0012483878 -0.0012294120
#> [251] -0.0012139212 -0.0012019155 -0.0011934233 -0.0011884731 -0.0011870649
#> [256] -0.0011913465 -0.0011986723 -0.0012069369 -0.0012166950 -0.0012269084
#> [261] -0.0012381460 -0.0012477334 -0.0012599241 -0.0012720293 -0.0012867093
#> [266] -0.0013018017 -0.0013188572 -0.0013346182 -0.0013506495 -0.0013674916
#> [271] -0.0013846182 -0.0014025414 -0.0014190848 -0.0014364105 -0.0014524276
#> [276] -0.0014703650 -0.0014865101 -0.0015019724 -0.0015152014 -0.0015283309
#> [281] -0.0015381460 -0.0015494404 -0.0015601232 -0.0015728970 -0.0015829824
#> [286] -0.0015941488 -0.0016043195 -0.0016141204 -0.0016230962 -0.0016313039
#> [291] -0.0016393408 -0.0016456709 -0.0016514319 -0.0016551019 -0.0016578330
#> [296] -0.0016586296 -0.0016612470 -0.0016656566 -0.0016696822 -0.0016711474
#> [301] -0.0016689283 -0.0016656282 -0.0016600948 -0.0016506068 -0.0016369938
#> [306] -0.0016191132 -0.0015979326 -0.0015744618 -0.0015502655 -0.0015263394
#> [311] -0.0014999525 -0.0014704930 -0.0014400379 -0.0014091132 -0.0013798672
#> [316] -0.0013507349 -0.0013227975 -0.0012966239 -0.0012722711 -0.0012497818
#> [321] -0.0012292270 -0.0012117590 -0.0011958558 -0.0011810336 -0.0011673209
#> [326] -0.0011558273 -0.0011460691 -0.0011359696 -0.0011266666 -0.0011187719
#> [331] -0.0011134233 -0.0011096111 -0.0011047036 -0.0010998103 -0.0010938928
#> [336] -0.0010885585 -0.0010822285 -0.0010760265 -0.0010705499 -0.0010647889
#> [341] -0.0010609482 -0.0010590848 -0.0010587150 -0.0010577477 -0.0010572925
#> [346] -0.0010573778 -0.0010585869 -0.0010588572 -0.0010566381 -0.0010546182
#> [351] -0.0010512327 -0.0010465386 -0.0010368515 -0.0010259411 -0.0010149027
#> [356] -0.0010026979 -0.0009909625 -0.0009781175 -0.0009668515 -0.0009539781
#> [361] -0.0009421573 -0.0009297676 -0.0009172783 -0.0009056851 -0.0008943906
#> [366] -0.0008822854 -0.0008677477 -0.0008518159 -0.0008344333 -0.0008160549
#> [371] -0.0007971360 -0.0007787292 -0.0007635371 -0.0007494404 -0.0007380037
#> [376] -0.0007270364 -0.0007181175 -0.0007091417 -0.0007017448 -0.0006975485
#> [381] -0.0006938928 -0.0006907918 -0.0006866666 -0.0006842057 -0.0006839923
#> [386] -0.0006849881 -0.0006861545 -0.0006901659 -0.0006948743 -0.0007034660
#> [391] -0.0007127121 -0.0007242199 -0.0007369226 -0.0007497676 -0.0007633238
#> [396] -0.0007760122 -0.0007889283 -0.0008020862 -0.0008160265 -0.0008302370
#> [401] -0.0008447462 -0.0008595827 -0.0008731531 -0.0008865101 -0.0008985869
#> [406] -0.0009120578 -0.0009248031 -0.0009368373 -0.0009470933 -0.0009571787
#> [411] -0.0009676339 -0.0009773921 -0.0009869511 -0.0009978615 -0.0010074205
#> [416] -0.0010166808 -0.0010246040 -0.0010333380 -0.0010407349 -0.0010488999
#> [421] -0.0010562256 -0.0010632384 -0.0010677761 -0.0010713892 -0.0010751019
#> [426] -0.0010772783 -0.0010768088 -0.0010725699 -0.0010676908 -0.0010616026
#> [431] -0.0010548031 -0.0010456709 -0.0010352726 -0.0010230820 -0.0010091132
#> [436] -0.0009955002 -0.0009822427 -0.0009725272 -0.0009652726 -0.0009588999
#> [441] -0.0009523565 -0.0009435087 -0.0009350165 -0.0009263536 -0.0009196680
#> [446] -0.0009144333 -0.0009090421 -0.0009040350 -0.0009004930 -0.0008995115
#> [451] -0.0008995400 -0.0009000805 -0.0009011189 -0.0009036794 -0.0009077619
#> [456] -0.0009123138 -0.0009183736 -0.0009248174 -0.0009337221 -0.0009418586
#> [461] -0.0009508344 -0.0009585016 -0.0009696538 -0.0009821431 -0.0009965243
#> [466] -0.0010112470 -0.0010257989 -0.0010412754 -0.0010555428 -0.0010707064
#> [471] -0.0010840777 -0.0010961829 -0.0011075627 -0.0011198103 -0.0011323565
#> [476] -0.0011430535 -0.0011534945 -0.0011615457 -0.0011709340 -0.0011805357
#> [481] -0.0011908487 -0.0011991986 -0.0012076908 -0.0012157420 -0.0012222569
#> [486] -0.0012288288 -0.0012354290 -0.0012441630 -0.0012523849 -0.0012601232
#> [491] -0.0012668373 -0.0012719582 -0.0012765386 -0.0012816310 -0.0012877477
#> [496] -0.0012932669 -0.0012976481 -0.0012993124 -0.0013009482 -0.0013015172
#> [501] -0.0013021004 -0.0013005642 -0.0012985016 -0.0012964532 -0.0012949454
#> [506] -0.0012934376 -0.0012903223 -0.0012855855 -0.0012776339 -0.0012670080
#> [511] -0.0012563821 -0.0012457562 -0.0012383451 -0.0012304646 -0.0012221431
#> [516] -0.0012128544 -0.0012010051 -0.0011903223 -0.0011797107 -0.0011707491
#> [521] -0.0011623707 -0.0011529966 -0.0011442199 -0.0011355002 -0.0011252726
#> [526] -0.0011135656 -0.0011014461 -0.0010910478 -0.0010797534 -0.0010670933
#> [531] -0.0010536367 -0.0010420862 -0.0010293124 -0.0010170222 -0.0010015741
#> [536] -0.0009877903 -0.0009730108 -0.0009599383 -0.0009486154 -0.0009374489
#> [541] -0.0009264248 -0.0009150023 -0.0009042342 -0.0008941061 -0.0008856851
#> [546] -0.0008763110 -0.0008675627 -0.0008572925 -0.0008481886 -0.0008392270
#> [551] -0.0008298814 -0.0008217306 -0.0008120862 -0.0008052014 -0.0007995115
#> [556] -0.0007950450 -0.0007891417 -0.0007855428 -0.0007837221 -0.0007815457
#> [561] -0.0007795542 -0.0007783167 -0.0007789425 -0.0007787719 -0.0007793835
#> [566] -0.0007797107 -0.0007813608 -0.0007832526 -0.0007864674 -0.0007894546
#> [571] -0.0007927975 -0.0007938643 -0.0007942484 -0.0007934091 -0.0007918871
#> [576] -0.0007902228 -0.0007873636 -0.0007849169 -0.0007828828 -0.0007828686
#> [581] -0.0007827548 -0.0007820293 -0.0007796538 -0.0007756282 -0.0007704646
#> [586] -0.0007668088 -0.0007641204 -0.0007629539 -0.0007590706 -0.0007541061
#> [591] -0.0007486154 -0.0007425841 -0.0007381460 -0.0007353295 -0.0007331104
#> [596] -0.0007315315 -0.0007284731 -0.0007266381 -0.0007239354 -0.0007193124
#> [601] -0.0007133095 -0.0007080748 -0.0007036367 -0.0006983878 -0.0006922854
#> [606] -0.0006879468 -0.0006826979 -0.0006749454 -0.0006636083 -0.0006497249
#> [611] -0.0006380748 -0.0006259838 -0.0006139781 -0.0006015457 -0.0005892555
#> [616] -0.0005792413 -0.0005704077 -0.0005627548 -0.0005547178 -0.0005463536
#> [621] -0.0005376908 -0.0005308771 -0.0005243053 -0.0005184874 -0.0005118159
#> [626] -0.0005053721 -0.0005002512 -0.0004959269 -0.0004934802 -0.0004907918
#> [631] -0.0004894689 -0.0004868515 -0.0004845755 -0.0004821716 -0.0004807349
#> [636] -0.0004797249 -0.0004801801 -0.0004815457 -0.0004827690 -0.0004849454
#> [641] -0.0004875485 -0.0004921716 -0.0004967093 -0.0005006637 -0.0005029824
#> [646] -0.0005047320 -0.0005069369 -0.0005095684 -0.0005126125 -0.0005139354
#> [651] -0.0005135798 -0.0005126268 -0.0005121431 -0.0005153010 -0.0005199383
#> [656] -0.0005249881 -0.0005277903 -0.0005294120 -0.0005314319 -0.0005343622
#> [661] -0.0005376908 -0.0005419866 -0.0005472498 -0.0005507918 -0.0005525841
#> [666] -0.0005542199 -0.0005572925 -0.0005612612 -0.0005655997 -0.0005687150
#> [671] -0.0005727263 -0.0005754859 -0.0005775201 -0.0005782882 -0.0005772498
#> [676] -0.0005749312 -0.0005713465 -0.0005681033 -0.0005662541 -0.0005657847
#> [681] -0.0005677761 -0.0005695542 -0.0005710763 -0.0005722854 -0.0005736652
#> [686] -0.0005778615 -0.0005837790 -0.0005919013 -0.0006000379 -0.0006076196
#> [691] -0.0006173209 -0.0006275770 -0.0006378757 -0.0006455571 -0.0006527263
#> [696] -0.0006609340 -0.0006727832 -0.0006850450 -0.0006977334 -0.0007103081
#> [701] -0.0007232669 -0.0007381602 -0.0007538928 -0.0007683451 -0.0007825983
#> [706] -0.0007966524 -0.0008104930 -0.0008224845 -0.0008325983 -0.0008440066
#> [711] -0.0008566808 -0.0008727263 -0.0008889283 -0.0009052583 -0.0009206211
#> [716] -0.0009360834 -0.0009521858 -0.0009678330 -0.0009834945 -0.0009969938
#> [721] -0.0010088572 -0.0010207206 -0.0010320720 -0.0010439781 -0.0010542768
#> [726] -0.0010634233 -0.0010724276 -0.0010807349 -0.0010915741 -0.0011028401
#> [731] -0.0011145187 -0.0011239070 -0.0011325699 -0.0011399810 -0.0011472356
#> [736] -0.0011527263 -0.0011585585 -0.0011668231 -0.0011742768 -0.0011824703
#> [741] -0.0011897534 -0.0011977192 -0.0012053010 -0.0012130108 -0.0012213181
#> [746] -0.0012312327 -0.0012405784 -0.0012498387 -0.0012584305 -0.0012662967
#> [751] -0.0012744333 -0.0012811616 -0.0012890848 -0.0012981602 -0.0013088856
#> [756] -0.0013190990 -0.0013293266 -0.0013400805 -0.0013513465 -0.0013625841
#> [761] -0.0013743337 -0.0013866239 -0.0014000236 -0.0014124418 -0.0014244191
#> [766] -0.0014359411 -0.0014464532 -0.0014548743 -0.0014611758 -0.0014674632
#> [771] -0.0014742768 -0.0014832669 -0.0014912612 -0.0014992982 -0.0015068231
#> [776] -0.0015143764 -0.0015225272 -0.0015307633 -0.0015369226 -0.0015415172
#> [781] -0.0015445471 -0.0015465528 -0.0015475343 -0.0015464105 -0.0015448031
#> [786] -0.0015422285 -0.0015403508 -0.0015376054 -0.0015350734 -0.0015311616
#> [791] -0.0015253437 -0.0015192270 -0.0015128259 -0.0015082882 -0.0015018728
#> [796] -0.0014951445 -0.0014864532 -0.0014768515 -0.0014652868 -0.0014528544
#> [801] -0.0014401232 -0.0014260691 -0.0014107206 -0.0013951445 -0.0013782882
#> [806] -0.0013607206 -0.0013408771 -0.0013209056 -0.0012991844 -0.0012756993
#> [811] -0.0012504646 -0.0012245755 -0.0012007491 -0.0011774063 -0.0011556282
#> [816] -0.0011333095 -0.0011088856 -0.0010829397 -0.0010565812 -0.0010320009
#> [821] -0.0010092413 -0.0009867377 -0.0009661118 -0.0009447320 -0.0009231957
#> [826] -0.0009015884 -0.0008805215 -0.0008605926 -0.0008418728 -0.0008254859
#> [831] -0.0008098672 -0.0007955571 -0.0007825841 -0.0007709767 -0.0007596965
#> [836] -0.0007482455 -0.0007377050 -0.0007270222 -0.0007167235 -0.0007052299
#> [841] -0.0006936652 -0.0006810194 -0.0006673352 -0.0006542342 -0.0006422711
#> [846] -0.0006314603 -0.0006202228 -0.0006107064 -0.0006018728 -0.0005942911
#> [851] -0.0005858558 -0.0005766097 -0.0005660834 -0.0005559411 -0.0005440919
#> [856] -0.0005326979 -0.0005201801 -0.0005076481 -0.0004935514 -0.0004774063
#> [861] -0.0004587292 -0.0004386296 -0.0004176908 -0.0003954433 -0.0003724845
#> [866] -0.0003483167 -0.0003261545 -0.0003049312 -0.0002862683 -0.0002691132
#> [871] -0.0002545471 -0.0002410051 -0.0002285016 -0.0002175485 -0.0002075770
#> [876] -0.0001969938 -0.0001852868
#>
#> $MO2_fish2$`Starting Times`
#> [1] 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58
#> [16] 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88
#> [31] 90 92 94 96 98 100 102 104 106 108 110 112 114 116 118
#> [46] 120 122 124 126 128 130 132 134 136 138 140 142 144 146 148
#> [61] 150 152 154 156 158 160 162 164 166 168 170 172 174 176 178
#> [76] 180 182 184 186 188 190 192 194 196 198 200 202 204 206 208
#> [91] 210 212 214 216 218 220 222 224 226 228 230 232 234 236 238
#> [106] 240 242 244 246 248 250 252 254 256 258 260 262 264 266 268
#> [121] 270 272 274 276 278 280 282 284 286 288 290 292 294 296 298
#> [136] 300 302 304 306 308 310 312 314 316 318 320 322 324 326 328
#> [151] 330 332 334 336 338 340 342 344 346 348 350 352 354 356 358
#> [166] 360 362 364 366 368 370 372 374 376 378 380 382 384 386 388
#> [181] 390 392 394 396 398 400 402 404 406 408 410 412 414 416 418
#> [196] 420 422 424 426 428 430 432 434 436 438 440 442 444 446 448
#> [211] 450 452 454 456 458 460 462 464 466 468 470 472 474 476 478
#> [226] 480 482 484 486 488 490 492 494 496 498 500 502 504 506 508
#> [241] 510 512 514 516 518 520 522 524 526 528 530 532 534 536 538
#> [256] 540 542 544 546 548 550 552 554 556 558 560 562 564 566 568
#> [271] 570 572 574 576 578 580 582 584 586 588 590 592 594 596 598
#> [286] 600 602 604 606 608 610 612 614 616 618 620 622 624 626 628
#> [301] 630 632 634 636 638 640 642 644 646 648 650 652 654 656 658
#> [316] 660 662 664 666 668 670 672 674 676 678 680 682 684 686 688
#> [331] 690 692 694 696 698 700 702 704 706 708 710 712 714 716 718
#> [346] 720 722 724 726 728 730 732 734 736 738 740 742 744 746 748
#> [361] 750 752 754 756 758 760 762 764 766 768 770 772 774 776 778
#> [376] 780 782 784 786 788 790 792 794 796 798 800 802 804 806 808
#> [391] 810 812 814 816 818 820 822 824 826 828 830 832 834 836 838
#> [406] 840 842 844 846 848 850 852 854 856 858 860 862 864 866 868
#> [421] 870 872 874 876 878 880 882 884 886 888 890 892 894 896 898
#> [436] 900 902 904 906 908 910 912 914 916 918 920 922 924 926 928
#> [451] 930 932 934 936 938 940 942 944 946 948 950 952 954 956 958
#> [466] 960 962 964 966 968 970 972 974 976 978 980 982 984 986 988
#> [481] 990 992 994 996 998 1000 1002 1004 1006 1008 1010 1012 1014 1016 1018
#> [496] 1020 1022 1024 1026 1028 1030 1032 1034 1036 1038 1040 1042 1044 1046 1048
#> [511] 1050 1052 1054 1056 1058 1060 1062 1064 1066 1068 1070 1072 1074 1076 1078
#> [526] 1080 1082 1084 1086 1088 1090 1092 1094 1096 1098 1100 1102 1104 1106 1108
#> [541] 1110 1112 1114 1116 1118 1120 1122 1124 1126 1128 1130 1132 1134 1136 1138
#> [556] 1140 1142 1144 1146 1148 1150 1152 1154 1156 1158 1160 1162 1164 1166 1168
#> [571] 1170 1172 1174 1176 1178 1180 1182 1184 1186 1188 1190 1192 1194 1196 1198
#> [586] 1200 1202 1204 1206 1208 1210 1212 1214 1216 1218 1220 1222 1224 1226 1228
#> [601] 1230 1232 1234 1236 1238 1240 1242 1244 1246 1248 1250 1252 1254 1256 1258
#> [616] 1260 1262 1264 1266 1268 1270 1272 1274 1276 1278 1280 1282 1284 1286 1288
#> [631] 1290 1292 1294 1296 1298 1300 1302 1304 1306 1308 1310 1312 1314 1316 1318
#> [646] 1320 1322 1324 1326 1328 1330 1332 1334 1336 1338 1340 1342 1344 1346 1348
#> [661] 1350 1352 1354 1356 1358 1360 1362 1364 1366 1368 1370 1372 1374 1376 1378
#> [676] 1380 1382 1384 1386 1388 1390 1392 1394 1396 1398 1400 1402 1404 1406 1408
#> [691] 1410 1412 1414 1416 1418 1420 1422 1424 1426 1428 1430 1432 1434 1436 1438
#> [706] 1440 1442 1444 1446 1448 1450 1452 1454 1456 1458 1460 1462 1464 1466 1468
#> [721] 1470 1472 1474 1476 1478 1480 1482 1484 1486 1488 1490 1492 1494 1496 1498
#> [736] 1500 1502 1504 1506 1508 1510 1512 1514 1516 1518 1520 1522 1524 1526 1528
#> [751] 1530 1532 1534 1536 1538 1540 1542 1544 1546 1548 1550 1552 1554 1556 1558
#> [766] 1560 1562 1564 1566 1568 1570 1572 1574 1576 1578 1580 1582 1584 1586 1588
#> [781] 1590 1592 1594 1596 1598 1600 1602 1604 1606 1608 1610 1612 1614 1616 1618
#> [796] 1620 1622 1624 1626 1628 1630 1632 1634 1636 1638 1640 1642 1644 1646 1648
#> [811] 1650 1652 1654 1656 1658 1660 1662 1664 1666 1668 1670 1672 1674 1676 1678
#> [826] 1680 1682 1684 1686 1688 1690 1692 1694 1696 1698 1700 1702 1704 1706 1708
#> [841] 1710 1712 1714 1716 1718 1720 1722 1724 1726 1728 1730 1732 1734 1736 1738
#> [856] 1740 1742 1744 1746 1748 1750 1752 1754 1756 1758 1760 1762 1764 1766 1768
#> [871] 1770 1772 1774 1776 1778 1780 1782
#>
#> $MO2_fish2$`Minimum Adjusted Slope`
#> [1] -0.004228857
#>
#> $MO2_fish2$`Percentage of Fish Slope (%)`
#> [1] 0.9147133
#>
#> $MO2_fish2$`Start Time for Minimum Slope`
#> [1] 86
#>
#> $MO2_fish2$`R-Squared for Minimum Slope`
#> [1] 0.9278818
#>
#> $MO2_fish2$`Mean R-Squared for all windows`
#> [1] 0.9523238
#>
#>
#> $MO2_fish3
#> $MO2_fish3$`Adjusted Slopes`
#> [1] -0.0032910905 -0.0033667519 -0.0034457704 -0.0035252441 -0.0036092413
#> [6] -0.0036944191 -0.0037838359 -0.0038751872 -0.0039715172 -0.0040674063
#> [11] -0.0041617590 -0.0042572498 -0.0043538643 -0.0044531389 -0.0045491559
#> [16] -0.0046429539 -0.0047318586 -0.0048169084 -0.0048975343 -0.0049737078
#> [21] -0.0050480464 -0.0051178472 -0.0051830393 -0.0052402655 -0.0052904219
#> [26] -0.0053355286 -0.0053771076 -0.0054150450 -0.0054497107 -0.0054814603
#> [31] -0.0055111758 -0.0055381175 -0.0055642484 -0.0055910336 -0.0056177903
#> [36] -0.0056448743 -0.0056715457 -0.0056965386 -0.0057180891 -0.0057355428
#> [41] -0.0057503935 -0.0057625130 -0.0057759980 -0.0057896253 -0.0058022285
#> [46] -0.0058106068 -0.0058158416 -0.0058189994 -0.0058190421 -0.0058139070
#> [51] -0.0058026268 -0.0057858273 -0.0057679184 -0.0057463821 -0.0057223849
#> [56] -0.0056933238 -0.0056630108 -0.0056299810 -0.0055954575 -0.0055564248
#> [61] -0.0055120151 -0.0054634518 -0.0054124418 -0.0053563963 -0.0052970222
#> [66] -0.0052339212 -0.0051720435 -0.0051072498 -0.0050428544 -0.0049805642
#> [71] -0.0049210194 -0.0048594831 -0.0047970364 -0.0047325841 -0.0046714177
#> [76] -0.0046129966 -0.0045600094 -0.0045135940 -0.0044743337 -0.0044438359
#> [81] -0.0044209909 -0.0044062967 -0.0043997534 -0.0043987292 -0.0044016168
#> [86] -0.0044056566 -0.0044097249 -0.0044143622 -0.0044180606 -0.0044198103
#> [91] -0.0044195969 -0.0044168373 -0.0044120151 -0.0044029824 -0.0043902655
#> [96] -0.0043749596 -0.0043560122 -0.0043355571 -0.0043125556 -0.0042870364
#> [101] -0.0042584874 -0.0042285300 -0.0041982740 -0.0041678046 -0.0041382882
#> [106] -0.0041103650 -0.0040862683 -0.0040634233 -0.0040435514 -0.0040267804
#> [111] -0.0040143195 -0.0040063252 -0.0040008202 -0.0039968657 -0.0039924276
#> [116] -0.0039897391 -0.0039883878 -0.0039912327 -0.0039963536 -0.0040028544
#> [121] -0.0040065528 -0.0040138643 -0.0040216026 -0.0040292697 -0.0040310336
#> [126] -0.0040285158 -0.0040211901 -0.0040085585 -0.0039912043 -0.0039713039
#> [131] -0.0039488856 -0.0039266097 -0.0039012470 -0.0038748885 -0.0038400379
#> [136] -0.0037988003 -0.0037501090 -0.0036988003 -0.0036411758 -0.0035778046
#> [141] -0.0035071218 -0.0034308060 -0.0033484162 -0.0032600521 -0.0031685158
#> [146] -0.0030739354 -0.0029774774 -0.0028792270 -0.0027814177 -0.0026836652
#> [151] -0.0025872071 -0.0024927263 -0.0023998387 -0.0023108487 -0.0022243906
#> [156] -0.0021439354 -0.0020681175 -0.0019992697 -0.0019374916 -0.0018849738
#> [161] -0.0018412470 -0.0018036652 -0.0017716879 -0.0017415315 -0.0017163536
#> [166] -0.0016950734 -0.0016798245 -0.0016705784 -0.0016646182 -0.0016634802
#> [171] -0.0016649596 -0.0016716595 -0.0016819297 -0.0016941488 -0.0017088288
#> [176] -0.0017259411 -0.0017465243 -0.0017684447 -0.0017922285 -0.0018173352
#> [181] -0.0018443053 -0.0018731815 -0.0019040066 -0.0019357278 -0.0019694404
#> [186] -0.0020035798 -0.0020382029 -0.0020707064 -0.0021027548 -0.0021322711
#> [191] -0.0021598672 -0.0021829824 -0.0022017164 -0.0022172071 -0.0022279326
#> [196] -0.0022345329 -0.0022403650 -0.0022455855 -0.0022481744 -0.0022460834
#> [201] -0.0022404361 -0.0022323565 -0.0022197391 -0.0022036794 -0.0021852868
#> [206] -0.0021677903 -0.0021522996 -0.0021367519 -0.0021207206 -0.0021010763
#> [211] -0.0020810194 -0.0020589141 -0.0020384447 -0.0020174205 -0.0019968231
#> [216] -0.0019749596 -0.0019506637 -0.0019249027 -0.0018949169 -0.0018611758
#> [221] -0.0018252299 -0.0017886296 -0.0017518586 -0.0017132953 -0.0016702797
#> [226] -0.0016254717 -0.0015788714 -0.0015320578 -0.0014849881 -0.0014349312
#> [231] -0.0013834233 -0.0013309625 -0.0012801801 -0.0012353437 -0.0011938359
#> [236] -0.0011578615 -0.0011269369 -0.0011026837 -0.0010824561 -0.0010673494
#> [241] -0.0010563252 -0.0010504788 -0.0010498814 -0.0010530108 -0.0010604930
#> [246] -0.0010708344 -0.0010857420 -0.0011026694 -0.0011201232 -0.0011387434
#> [251] -0.0011602228 -0.0011862256 -0.0012130677 -0.0012418871 -0.0012706353
#> [256] -0.0013020435 -0.0013319013 -0.0013650592 -0.0013973067 -0.0014329539
#> [261] -0.0014667093 -0.0014996965 -0.0015298103 -0.0015591986 -0.0015862683
#> [266] -0.0016104788 -0.0016296680 -0.0016474916 -0.0016643906 -0.0016802512
#> [271] -0.0016933380 -0.0017045898 -0.0017149596 -0.0017222285 -0.0017278899
#> [276] -0.0017323565 -0.0017376339 -0.0017409482 -0.0017416879 -0.0017397960
#> [281] -0.0017400236 -0.0017407206 -0.0017412896 -0.0017389710 -0.0017352726
#> [286] -0.0017317306 -0.0017298956 -0.0017302655 -0.0017317733 -0.0017339212
#> [291] -0.0017372783 -0.0017423707 -0.0017491844 -0.0017566239 -0.0017614603
#> [296] -0.0017653010 -0.0017692270 -0.0017732669 -0.0017774063 -0.0017794404
#> [301] -0.0017803935 -0.0017770791 -0.0017732811 -0.0017690421 -0.0017659696
#> [306] -0.0017608771 -0.0017559269 -0.0017516879 -0.0017503508 -0.0017477050
#> [311] -0.0017448885 -0.0017425272 -0.0017439070 -0.0017469511 -0.0017517164
#> [316] -0.0017582598 -0.0017676908 -0.0017773778 -0.0017868373 -0.0017955713
#> [321] -0.0018041204 -0.0018130108 -0.0018200948 -0.0018232384 -0.0018235087
#> [326] -0.0018230393 -0.0018234518 -0.0018221004 -0.0018179326 -0.0018109482
#> [331] -0.0018027548 -0.0017912185 -0.0017784874 -0.0017613892 -0.0017452726
#> [336] -0.0017242768 -0.0016994689 -0.0016703223 -0.0016394973 -0.0016064390
#> [341] -0.0015690137 -0.0015277619 -0.0014859127 -0.0014451019 -0.0014064248
#> [346] -0.0013683167 -0.0013313465 -0.0012950307 -0.0012588572 -0.0012239212
#> [351] -0.0011913323 -0.0011616879 -0.0011329113 -0.0011039639 -0.0010753864
#> [356] -0.0010503935 -0.0010268942 -0.0010059980 -0.0009861403 -0.0009684162
#> [361] -0.0009502228 -0.0009358985 -0.0009249454 -0.0009168373 -0.0009083736
#> [366] -0.0009017022 -0.0008979184 -0.0008970507 -0.0008986012 -0.0008999241
#> [371] -0.0009010194 -0.0009034945 -0.0009057562 -0.0009110336 -0.0009177619
#> [376] -0.0009264817 -0.0009328970 -0.0009380606 -0.0009425272 -0.0009495258
#> [381] -0.0009563821 -0.0009646751 -0.0009722854 -0.0009813750 -0.0009887576
#> [386] -0.0009965386 -0.0010041346 -0.0010131389 -0.0010219866 -0.0010312327
#> [391] -0.0010403650 -0.0010510194 -0.0010610905 -0.0010695258 -0.0010763536
#> [396] -0.0010832100 -0.0010922427 -0.0011034233 -0.0011129682 -0.0011224418
#> [401] -0.0011307775 -0.0011401232 -0.0011478188 -0.0011554433 -0.0011629539
#> [406] -0.0011729539 -0.0011837647 -0.0011926837 -0.0011986296 -0.0012053295
#> [411] -0.0012122000 -0.0012201943 -0.0012270791 -0.0012322996 -0.0012364105
#> [416] -0.0012367377 -0.0012369653 -0.0012349169 -0.0012343195 -0.0012335798
#> [421] -0.0012321858 -0.0012285727 -0.0012227832 -0.0012154006 -0.0012069938
#> [426] -0.0011991844 -0.0011914461 -0.0011843053 -0.0011772071 -0.0011701517
#> [431] -0.0011621289 -0.0011531957 -0.0011439212 -0.0011343337 -0.0011244618
#> [436] -0.0011121716 -0.0011011901 -0.0010910194 -0.0010811901 -0.0010712043
#> [441] -0.0010616026 -0.0010545187 -0.0010488856 -0.0010425983 -0.0010341061
#> [446] -0.0010245187 -0.0010159838 -0.0010095400 -0.0010046182 -0.0010027832
#> [451] -0.0010039923 -0.0010076623 -0.0010115884 -0.0010146609 -0.0010195258
#> [456] -0.0010261687 -0.0010340492 -0.0010415457 -0.0010496965 -0.0010584589
#> [461] -0.0010683451 -0.0010793693 -0.0010920435 -0.0011052441 -0.0011184020
#> [466] -0.0011293835 -0.0011392697 -0.0011475343 -0.0011552157 -0.0011601374
#> [471] -0.0011638785 -0.0011664674 -0.0011711616 -0.0011742626 -0.0011758131
#> [476] -0.0011747747 -0.0011732953 -0.0011692555 -0.0011653437 -0.0011621147
#> [481] -0.0011612185 -0.0011606068 -0.0011581886 -0.0011539639 -0.0011490137
#> [486] -0.0011450023 -0.0011446751 -0.0011454148 -0.0011445613 -0.0011426410
#> [491] -0.0011385869 -0.0011356282 -0.0011311332 -0.0011277903 -0.0011240208
#> [496] -0.0011187719 -0.0011109767 -0.0011011474 -0.0010903081 -0.0010773778
#> [501] -0.0010634518 -0.0010485727 -0.0010359554 -0.0010266381 -0.0010194973
#> [506] -0.0010102086 -0.0009992839 -0.0009861972 -0.0009720293 -0.0009578330
#> [511] -0.0009414746 -0.0009261687 -0.0009092555 -0.0008896822 -0.0008690706
#> [516] -0.0008463963 -0.0008254433 -0.0008020151 -0.0007761687 -0.0007468373
#> [521] -0.0007166666 -0.0006877761 -0.0006607349 -0.0006350592 -0.0006123992
#> [526] -0.0005933380 -0.0005789710 -0.0005682455 -0.0005605926 -0.0005586154
#> [531] -0.0005617164 -0.0005692982 -0.0005786581 -0.0005908487 -0.0006053437
#> [536] -0.0006237221 -0.0006443337 -0.0006692982 -0.0006954433 -0.0007233238
#> [541] -0.0007513039 -0.0007788145 -0.0008063679 -0.0008366097 -0.0008694831
#> [546] -0.0009049027 -0.0009411758 -0.0009792982 -0.0010155144 -0.0010487434
#> [551] -0.0010789852 -0.0011083736 -0.0011347747 -0.0011582171 -0.0011814034
#> [556] -0.0012038074 -0.0012254006 -0.0012461403 -0.0012654717 -0.0012833807
#> [561] -0.0013003650 -0.0013185300 -0.0013367946 -0.0013546040 -0.0013724561
#> [566] -0.0013908202 -0.0014090990 -0.0014267093 -0.0014436225 -0.0014592982
#> [571] -0.0014726552 -0.0014836794 -0.0014934233 -0.0015024276 -0.0015090848
#> [576] -0.0015123138 -0.0015126410 -0.0015132811 -0.0015158842 -0.0015199810
#> [581] -0.0015229397 -0.0015252726 -0.0015248174 -0.0015226268 -0.0015203081
#> [586] -0.0015157135 -0.0015109625 -0.0015028686 -0.0014924845 -0.0014787292
#> [591] -0.0014636936 -0.0014478615 -0.0014279895 -0.0014045613 -0.0013780891
#> [596] -0.0013506922 -0.0013229113 -0.0012942057 -0.0012645613 -0.0012345044
#> [601] -0.0012040492 -0.0011722000 -0.0011401090 -0.0011089283 -0.0010814177
#> [606] -0.0010555571 -0.0010320009 -0.0010097391 -0.0009893266 -0.0009686581
#> [611] -0.0009488856 -0.0009290563 -0.0009103223 -0.0008905926 -0.0008709625
#> [616] -0.0008520151 -0.0008327121 -0.0008130820 -0.0007947747 -0.0007789425
#> [621] -0.0007662114 -0.0007561260 -0.0007466381 -0.0007372498 -0.0007263536
#> [626] -0.0007144618 -0.0007015741 -0.0006871645 -0.0006701374 -0.0006526125
#> [631] -0.0006356566 -0.0006187576 -0.0006014319 -0.0005831815 -0.0005656140
#> [636] -0.0005487434 -0.0005336652 -0.0005198672 -0.0005078899 -0.0004977477
#> [641] -0.0004894973 -0.0004826694 -0.0004778330 -0.0004734091 -0.0004704646
#> [646] -0.0004689710 -0.0004699383 -0.0004727832 -0.0004764105 -0.0004813323
#> [651] -0.0004869653 -0.0004932526 -0.0005012185 -0.0005103081 -0.0005220862
#> [656] -0.0005343764 -0.0005492982 -0.0005646893 -0.0005815741 -0.0005999098
#> [661] -0.0006201943 -0.0006423849 -0.0006642911 -0.0006874632 -0.0007086723
#> [666] -0.0007321858 -0.0007558842 -0.0007808202 -0.0008053579 -0.0008294689
#> [671] -0.0008531531 -0.0008764248 -0.0009003650 -0.0009255002 -0.0009523423
#> [676] -0.0009803223 -0.0010061829 -0.0010304219 -0.0010535514 -0.0010776908
#> [681] -0.0010996253 -0.0011198814 -0.0011406068 -0.0011618159 -0.0011808629
#> [686] -0.0011956282 -0.0012082313 -0.0012202370 -0.0012310905 -0.0012391986
#> [691] -0.0012445898 -0.0012494120 -0.0012525841 -0.0012551588 -0.0012560549
#> [696] -0.0012547605 -0.0012502655 -0.0012404930 -0.0012302797 -0.0012190848
#> [701] -0.0012079610 -0.0011942342 -0.0011779184 -0.0011585158 -0.0011371218
#> [706] -0.0011158842 -0.0010926694 -0.0010679895 -0.0010418159 -0.0010157420
#> [711] -0.0009881744 -0.0009601943 -0.0009312896 -0.0009025556 -0.0008734802
#> [716] -0.0008424703 -0.0008100663 -0.0007784020 -0.0007469226 -0.0007166808
#> [721] -0.0006882171 -0.0006610478 -0.0006352299 -0.0006097249 -0.0005850876
#> [726] -0.0005624134 -0.0005406637 -0.0005198814 -0.0004995684 -0.0004813750
#> [731] -0.0004637505 -0.0004477903 -0.0004324703 -0.0004194262 -0.0004076339
#> [736] -0.0003976765 -0.0003906637 -0.0003860834 -0.0003844760 -0.0003831673
#> [741] -0.0003848031 -0.0003877619 -0.0003931104 -0.0003992697 -0.0004073067
#> [746] -0.0004161260 -0.0004267519 -0.0004380891 -0.0004517448 -0.0004656282
#> [751] -0.0004787719 -0.0004923138 -0.0005041488 -0.0005164390 -0.0005292128
#> [756] -0.0005419582 -0.0005530820 -0.0005631531 -0.0005727548 -0.0005824134
#> [761] -0.0005888999 -0.0005954148 -0.0006009340 -0.0006060407 -0.0006080891
#> [766] -0.0006081460 -0.0006083451 -0.0006102797 -0.0006128686 -0.0006150592
#> [771] -0.0006147178 -0.0006123423 -0.0006073209 -0.0006022711 -0.0005987861
#> [776] -0.0005963252 -0.0005938074 -0.0005895969 -0.0005841773 -0.0005775058
#> [781] -0.0005722285 -0.0005661972 -0.0005604788 -0.0005555997 -0.0005520720
#> [786] -0.0005482740 -0.0005436652 -0.0005377192 -0.0005320293 -0.0005271076
#> [791] -0.0005218586 -0.0005152014 -0.0005092839 -0.0005041346 -0.0005008344
#> [796] -0.0004978046 -0.0004945187 -0.0004920293 -0.0004913750 -0.0004914603
#> [801] -0.0004922854 -0.0004927975 -0.0004924703 -0.0004934233 -0.0004935087
#> [806] -0.0004948458 -0.0004963536 -0.0004985443 -0.0005014034 -0.0005049169
#> [811] -0.0005095827 -0.0005153864 -0.0005233949 -0.0005346609 -0.0005480891
#> [816] -0.0005642057 -0.0005809056 -0.0005992697 -0.0006182171 -0.0006398672
#> [821] -0.0006626268 -0.0006875912 -0.0007131815 -0.0007388856 -0.0007641630
#> [826] -0.0007862825 -0.0008089283 -0.0008278188 -0.0008472071 -0.0008654717
#> [831] -0.0008852441 -0.0009053864 -0.0009247462 -0.0009432526 -0.0009619582
#> [836] -0.0009781886 -0.0009951019 -0.0010104930 -0.0010253579 -0.0010359269
#> [841] -0.0010458985 -0.0010552299 -0.0010655144 -0.0010762114 -0.0010867377
#> [846] -0.0010959696 -0.0011028259 -0.0011084020 -0.0011148174 -0.0011220435
#> [851] -0.0011295400 -0.0011357135 -0.0011416310 -0.0011472783 -0.0011526410
#> [856] -0.0011587719 -0.0011645898 -0.0011705926 -0.0011762398 -0.0011810336
#> [861] -0.0011855286 -0.0011891701 -0.0011924418 -0.0011963679 -0.0012019866
#> [866] -0.0012076765 -0.0012118159 -0.0012159696 -0.0012200948 -0.0012252299
#> [871] -0.0012281602 -0.0012304788 -0.0012316595 -0.0012343906 -0.0012370791
#> [876] -0.0012386581 -0.0012385869
#>
#> $MO2_fish3$`Starting Times`
#> [1] 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58
#> [16] 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88
#> [31] 90 92 94 96 98 100 102 104 106 108 110 112 114 116 118
#> [46] 120 122 124 126 128 130 132 134 136 138 140 142 144 146 148
#> [61] 150 152 154 156 158 160 162 164 166 168 170 172 174 176 178
#> [76] 180 182 184 186 188 190 192 194 196 198 200 202 204 206 208
#> [91] 210 212 214 216 218 220 222 224 226 228 230 232 234 236 238
#> [106] 240 242 244 246 248 250 252 254 256 258 260 262 264 266 268
#> [121] 270 272 274 276 278 280 282 284 286 288 290 292 294 296 298
#> [136] 300 302 304 306 308 310 312 314 316 318 320 322 324 326 328
#> [151] 330 332 334 336 338 340 342 344 346 348 350 352 354 356 358
#> [166] 360 362 364 366 368 370 372 374 376 378 380 382 384 386 388
#> [181] 390 392 394 396 398 400 402 404 406 408 410 412 414 416 418
#> [196] 420 422 424 426 428 430 432 434 436 438 440 442 444 446 448
#> [211] 450 452 454 456 458 460 462 464 466 468 470 472 474 476 478
#> [226] 480 482 484 486 488 490 492 494 496 498 500 502 504 506 508
#> [241] 510 512 514 516 518 520 522 524 526 528 530 532 534 536 538
#> [256] 540 542 544 546 548 550 552 554 556 558 560 562 564 566 568
#> [271] 570 572 574 576 578 580 582 584 586 588 590 592 594 596 598
#> [286] 600 602 604 606 608 610 612 614 616 618 620 622 624 626 628
#> [301] 630 632 634 636 638 640 642 644 646 648 650 652 654 656 658
#> [316] 660 662 664 666 668 670 672 674 676 678 680 682 684 686 688
#> [331] 690 692 694 696 698 700 702 704 706 708 710 712 714 716 718
#> [346] 720 722 724 726 728 730 732 734 736 738 740 742 744 746 748
#> [361] 750 752 754 756 758 760 762 764 766 768 770 772 774 776 778
#> [376] 780 782 784 786 788 790 792 794 796 798 800 802 804 806 808
#> [391] 810 812 814 816 818 820 822 824 826 828 830 832 834 836 838
#> [406] 840 842 844 846 848 850 852 854 856 858 860 862 864 866 868
#> [421] 870 872 874 876 878 880 882 884 886 888 890 892 894 896 898
#> [436] 900 902 904 906 908 910 912 914 916 918 920 922 924 926 928
#> [451] 930 932 934 936 938 940 942 944 946 948 950 952 954 956 958
#> [466] 960 962 964 966 968 970 972 974 976 978 980 982 984 986 988
#> [481] 990 992 994 996 998 1000 1002 1004 1006 1008 1010 1012 1014 1016 1018
#> [496] 1020 1022 1024 1026 1028 1030 1032 1034 1036 1038 1040 1042 1044 1046 1048
#> [511] 1050 1052 1054 1056 1058 1060 1062 1064 1066 1068 1070 1072 1074 1076 1078
#> [526] 1080 1082 1084 1086 1088 1090 1092 1094 1096 1098 1100 1102 1104 1106 1108
#> [541] 1110 1112 1114 1116 1118 1120 1122 1124 1126 1128 1130 1132 1134 1136 1138
#> [556] 1140 1142 1144 1146 1148 1150 1152 1154 1156 1158 1160 1162 1164 1166 1168
#> [571] 1170 1172 1174 1176 1178 1180 1182 1184 1186 1188 1190 1192 1194 1196 1198
#> [586] 1200 1202 1204 1206 1208 1210 1212 1214 1216 1218 1220 1222 1224 1226 1228
#> [601] 1230 1232 1234 1236 1238 1240 1242 1244 1246 1248 1250 1252 1254 1256 1258
#> [616] 1260 1262 1264 1266 1268 1270 1272 1274 1276 1278 1280 1282 1284 1286 1288
#> [631] 1290 1292 1294 1296 1298 1300 1302 1304 1306 1308 1310 1312 1314 1316 1318
#> [646] 1320 1322 1324 1326 1328 1330 1332 1334 1336 1338 1340 1342 1344 1346 1348
#> [661] 1350 1352 1354 1356 1358 1360 1362 1364 1366 1368 1370 1372 1374 1376 1378
#> [676] 1380 1382 1384 1386 1388 1390 1392 1394 1396 1398 1400 1402 1404 1406 1408
#> [691] 1410 1412 1414 1416 1418 1420 1422 1424 1426 1428 1430 1432 1434 1436 1438
#> [706] 1440 1442 1444 1446 1448 1450 1452 1454 1456 1458 1460 1462 1464 1466 1468
#> [721] 1470 1472 1474 1476 1478 1480 1482 1484 1486 1488 1490 1492 1494 1496 1498
#> [736] 1500 1502 1504 1506 1508 1510 1512 1514 1516 1518 1520 1522 1524 1526 1528
#> [751] 1530 1532 1534 1536 1538 1540 1542 1544 1546 1548 1550 1552 1554 1556 1558
#> [766] 1560 1562 1564 1566 1568 1570 1572 1574 1576 1578 1580 1582 1584 1586 1588
#> [781] 1590 1592 1594 1596 1598 1600 1602 1604 1606 1608 1610 1612 1614 1616 1618
#> [796] 1620 1622 1624 1626 1628 1630 1632 1634 1636 1638 1640 1642 1644 1646 1648
#> [811] 1650 1652 1654 1656 1658 1660 1662 1664 1666 1668 1670 1672 1674 1676 1678
#> [826] 1680 1682 1684 1686 1688 1690 1692 1694 1696 1698 1700 1702 1704 1706 1708
#> [841] 1710 1712 1714 1716 1718 1720 1722 1724 1726 1728 1730 1732 1734 1736 1738
#> [856] 1740 1742 1744 1746 1748 1750 1752 1754 1756 1758 1760 1762 1764 1766 1768
#> [871] 1770 1772 1774 1776 1778 1780 1782
#>
#> $MO2_fish3$`Minimum Adjusted Slope`
#> [1] -0.005819042
#>
#> $MO2_fish3$`Percentage of Fish Slope (%)`
#> [1] 0.6647472
#>
#> $MO2_fish3$`Start Time for Minimum Slope`
#> [1] 126
#>
#> $MO2_fish3$`R-Squared for Minimum Slope`
#> [1] 0.9955683
#>
#> $MO2_fish3$`Mean R-Squared for all windows`
#> [1] 0.9262217
#>
#>
#> $MO2_fish4
#> $MO2_fish4$`Adjusted Slopes`
#> [1] -2.909924e-03 -2.857520e-03 -2.799170e-03 -2.736553e-03 -2.662826e-03
#> [6] -2.580152e-03 -2.486908e-03 -2.392186e-03 -2.298260e-03 -2.211204e-03
#> [11] -2.132271e-03 -2.060536e-03 -1.993452e-03 -1.932157e-03 -1.871873e-03
#> [16] -1.815827e-03 -1.761972e-03 -1.713622e-03 -1.669298e-03 -1.630706e-03
#> [21] -1.595799e-03 -1.567861e-03 -1.538971e-03 -1.514519e-03 -1.492342e-03
#> [26] -1.476083e-03 -1.464078e-03 -1.458985e-03 -1.461873e-03 -1.473281e-03
#> [31] -1.492172e-03 -1.518587e-03 -1.548302e-03 -1.582883e-03 -1.624917e-03
#> [36] -1.679611e-03 -1.746780e-03 -1.826183e-03 -1.916453e-03 -2.012413e-03
#> [41] -2.109981e-03 -2.204661e-03 -2.296311e-03 -2.382143e-03 -2.462086e-03
#> [46] -2.534007e-03 -2.598985e-03 -2.661290e-03 -2.723054e-03 -2.782712e-03
#> [51] -2.838715e-03 -2.893807e-03 -2.953950e-03 -3.014419e-03 -3.070977e-03
#> [56] -3.122001e-03 -3.169597e-03 -3.212698e-03 -3.250721e-03 -3.283096e-03
#> [61] -3.313011e-03 -3.345273e-03 -3.380962e-03 -3.421076e-03 -3.461802e-03
#> [66] -3.500436e-03 -3.537563e-03 -3.569042e-03 -3.597677e-03 -3.621460e-03
#> [71] -3.641517e-03 -3.656211e-03 -3.663381e-03 -3.661460e-03 -3.651588e-03
#> [76] -3.634874e-03 -3.615031e-03 -3.590934e-03 -3.564063e-03 -3.532086e-03
#> [81] -3.496937e-03 -3.456339e-03 -3.410763e-03 -3.359640e-03 -3.305088e-03
#> [86] -3.249284e-03 -3.192285e-03 -3.133594e-03 -3.075386e-03 -3.014519e-03
#> [91] -2.950009e-03 -2.880337e-03 -2.807164e-03 -2.731603e-03 -2.655273e-03
#> [96] -2.581873e-03 -2.511332e-03 -2.440891e-03 -2.372058e-03 -2.309583e-03
#> [101] -2.256595e-03 -2.210877e-03 -2.169170e-03 -2.135714e-03 -2.107805e-03
#> [106] -2.086994e-03 -2.071603e-03 -2.060991e-03 -2.058786e-03 -2.061247e-03
#> [111] -2.071204e-03 -2.087335e-03 -2.108416e-03 -2.139099e-03 -2.177605e-03
#> [116] -2.221019e-03 -2.264177e-03 -2.306652e-03 -2.347449e-03 -2.382883e-03
#> [121] -2.411375e-03 -2.432413e-03 -2.451375e-03 -2.469896e-03 -2.489540e-03
#> [126] -2.507036e-03 -2.525500e-03 -2.547008e-03 -2.570450e-03 -2.593622e-03
#> [131] -2.618558e-03 -2.645671e-03 -2.672186e-03 -2.698587e-03 -2.721631e-03
#> [136] -2.743438e-03 -2.762997e-03 -2.782556e-03 -2.803893e-03 -2.827136e-03
#> [141] -2.848601e-03 -2.870906e-03 -2.890678e-03 -2.910920e-03 -2.929298e-03
#> [146] -2.947278e-03 -2.965842e-03 -2.982726e-03 -2.993509e-03 -2.999099e-03
#> [151] -2.999384e-03 -2.998587e-03 -2.993523e-03 -2.988530e-03 -2.981062e-03
#> [156] -2.972854e-03 -2.959753e-03 -2.942356e-03 -2.921745e-03 -2.898971e-03
#> [161] -2.872954e-03 -2.843182e-03 -2.809696e-03 -2.774135e-03 -2.739113e-03
#> [166] -2.704462e-03 -2.670507e-03 -2.634419e-03 -2.596126e-03 -2.556667e-03
#> [171] -2.522968e-03 -2.492911e-03 -2.464419e-03 -2.437036e-03 -2.412997e-03
#> [176] -2.396709e-03 -2.383551e-03 -2.373139e-03 -2.370877e-03 -2.377321e-03
#> [181] -2.390379e-03 -2.405941e-03 -2.422115e-03 -2.440792e-03 -2.457933e-03
#> [186] -2.472058e-03 -2.481631e-03 -2.485017e-03 -2.485913e-03 -2.482712e-03
#> [191] -2.475955e-03 -2.464590e-03 -2.449682e-03 -2.429654e-03 -2.405031e-03
#> [196] -2.376283e-03 -2.344362e-03 -2.308132e-03 -2.269739e-03 -2.230337e-03
#> [201] -2.188957e-03 -2.147307e-03 -2.106041e-03 -2.066325e-03 -2.028758e-03
#> [206] -1.994476e-03 -1.966752e-03 -1.941887e-03 -1.917776e-03 -1.891802e-03
#> [211] -1.864533e-03 -1.834362e-03 -1.800209e-03 -1.762058e-03 -1.725756e-03
#> [216] -1.689654e-03 -1.659597e-03 -1.632442e-03 -1.609896e-03 -1.592058e-03
#> [221] -1.577378e-03 -1.569071e-03 -1.563452e-03 -1.557933e-03 -1.552584e-03
#> [226] -1.547975e-03 -1.544120e-03 -1.536780e-03 -1.527577e-03 -1.514903e-03
#> [231] -1.499270e-03 -1.478004e-03 -1.453238e-03 -1.428174e-03 -1.401247e-03
#> [236] -1.374618e-03 -1.345073e-03 -1.315770e-03 -1.285600e-03 -1.259938e-03
#> [241] -1.243125e-03 -1.235216e-03 -1.232513e-03 -1.230763e-03 -1.230009e-03
#> [246] -1.232485e-03 -1.237250e-03 -1.239611e-03 -1.244419e-03 -1.247975e-03
#> [251] -1.255671e-03 -1.262712e-03 -1.272285e-03 -1.288160e-03 -1.310450e-03
#> [256] -1.336069e-03 -1.359711e-03 -1.379199e-03 -1.392883e-03 -1.403395e-03
#> [261] -1.408061e-03 -1.411133e-03 -1.411546e-03 -1.411972e-03 -1.409753e-03
#> [266] -1.403850e-03 -1.393765e-03 -1.380081e-03 -1.364391e-03 -1.346154e-03
#> [271] -1.329127e-03 -1.313352e-03 -1.297250e-03 -1.280265e-03 -1.261304e-03
#> [276] -1.239810e-03 -1.214718e-03 -1.188687e-03 -1.161716e-03 -1.135443e-03
#> [281] -1.107264e-03 -1.082598e-03 -1.061489e-03 -1.045486e-03 -1.030749e-03
#> [286] -1.017207e-03 -1.004291e-03 -9.929966e-04 -9.837505e-04 -9.796822e-04
#> [291] -9.786581e-04 -9.828401e-04 -9.874489e-04 -9.940777e-04 -1.001674e-03
#> [296] -1.012399e-03 -1.026311e-03 -1.037634e-03 -1.046951e-03 -1.054803e-03
#> [301] -1.064917e-03 -1.072527e-03 -1.075031e-03 -1.075130e-03 -1.074433e-03
#> [306] -1.072399e-03 -1.067933e-03 -1.061005e-03 -1.054291e-03 -1.044063e-03
#> [311] -1.033551e-03 -1.020664e-03 -1.007605e-03 -9.933949e-04 -9.775770e-04
#> [316] -9.655997e-04 -9.565243e-04 -9.525699e-04 -9.494973e-04 -9.467804e-04
#> [321] -9.412470e-04 -9.361118e-04 -9.297107e-04 -9.262683e-04 -9.209767e-04
#> [326] -9.154717e-04 -9.108771e-04 -9.046040e-04 -8.983878e-04 -8.902086e-04
#> [331] -8.843764e-04 -8.824276e-04 -8.837221e-04 -8.875912e-04 -8.965386e-04
#> [336] -9.098103e-04 -9.293266e-04 -9.480322e-04 -9.690706e-04 -9.865528e-04
#> [341] -1.003622e-03 -1.015941e-03 -1.025059e-03 -1.033096e-03 -1.038971e-03
#> [346] -1.044803e-03 -1.051105e-03 -1.057847e-03 -1.065017e-03 -1.071005e-03
#> [351] -1.077904e-03 -1.084021e-03 -1.089312e-03 -1.093238e-03 -1.094206e-03
#> [356] -1.092200e-03 -1.086695e-03 -1.082556e-03 -1.079896e-03 -1.079384e-03
#> [361] -1.077378e-03 -1.077662e-03 -1.078174e-03 -1.081176e-03 -1.083622e-03
#> [366] -1.087762e-03 -1.094177e-03 -1.102883e-03 -1.115457e-03 -1.128687e-03
#> [371] -1.144704e-03 -1.160322e-03 -1.172854e-03 -1.183338e-03 -1.194419e-03
#> [376] -1.207691e-03 -1.217805e-03 -1.222584e-03 -1.223096e-03 -1.222044e-03
#> [381] -1.221034e-03 -1.218473e-03 -1.214903e-03 -1.209796e-03 -1.202627e-03
#> [386] -1.193907e-03 -1.182029e-03 -1.168032e-03 -1.151873e-03 -1.134021e-03
#> [391] -1.114960e-03 -1.095201e-03 -1.074775e-03 -1.056894e-03 -1.039384e-03
#> [396] -1.021631e-03 -1.003054e-03 -9.879184e-04 -9.761829e-04 -9.661829e-04
#> [401] -9.546324e-04 -9.425556e-04 -9.282740e-04 -9.121858e-04 -8.952157e-04
#> [406] -8.794120e-04 -8.669511e-04 -8.557989e-04 -8.476765e-04 -8.427406e-04
#> [411] -8.438643e-04 -8.512327e-04 -8.611901e-04 -8.726979e-04 -8.820435e-04
#> [416] -8.919297e-04 -9.013039e-04 -9.090848e-04 -9.152868e-04 -9.199383e-04
#> [421] -9.236083e-04 -9.284731e-04 -9.335087e-04 -9.377050e-04 -9.405784e-04
#> [426] -9.448600e-04 -9.495827e-04 -9.537363e-04 -9.562967e-04 -9.588572e-04
#> [431] -9.608629e-04 -9.606922e-04 -9.604504e-04 -9.574632e-04 -9.538501e-04
#> [436] -9.469369e-04 -9.399525e-04 -9.297249e-04 -9.183736e-04 -9.042342e-04
#> [441] -8.899241e-04 -8.749312e-04 -8.614319e-04 -8.505499e-04 -8.407206e-04
#> [446] -8.314461e-04 -8.222142e-04 -8.124845e-04 -8.027690e-04 -7.946609e-04
#> [451] -7.887007e-04 -7.853721e-04 -7.813892e-04 -7.766524e-04 -7.705499e-04
#> [456] -7.662541e-04 -7.605357e-04 -7.560265e-04 -7.521289e-04 -7.540350e-04
#> [461] -7.610336e-04 -7.697676e-04 -7.790848e-04 -7.852583e-04 -7.894262e-04
#> [466] -7.911189e-04 -7.882882e-04 -7.799525e-04 -7.688999e-04 -7.573921e-04
#> [471] -7.444760e-04 -7.270791e-04 -7.090563e-04 -6.937363e-04 -6.822569e-04
#> [476] -6.741061e-04 -6.655571e-04 -6.609056e-04 -6.617448e-04 -6.722854e-04
#> [481] -6.881886e-04 -7.056851e-04 -7.215741e-04 -7.363963e-04 -7.469795e-04
#> [486] -7.549596e-04 -7.609340e-04 -7.655428e-04 -7.678188e-04 -7.657135e-04
#> [491] -7.608913e-04 -7.550165e-04 -7.470791e-04 -7.376339e-04 -7.245755e-04
#> [496] -7.100663e-04 -6.941346e-04 -6.784020e-04 -6.612470e-04 -6.421289e-04
#> [501] -6.231673e-04 -6.022000e-04 -5.818728e-04 -5.594831e-04 -5.382455e-04
#> [506] -5.155286e-04 -4.940066e-04 -4.736652e-04 -4.539354e-04 -4.342626e-04
#> [511] -4.135656e-04 -3.923423e-04 -3.710763e-04 -3.502655e-04 -3.304504e-04
#> [516] -3.100521e-04 -2.890706e-04 -2.690848e-04 -2.537647e-04 -2.430108e-04
#> [521] -2.335371e-04 -2.210905e-04 -2.052299e-04 -1.871218e-04 -1.694973e-04
#> [526] -1.513465e-04 -1.333380e-04 -1.155997e-04 -1.003366e-04 -8.545748e-05
#> [531] -7.101936e-05 -5.760542e-05 -4.736360e-05 -3.981026e-05 -3.935507e-05
#> [536] -4.670926e-05 -5.934084e-05 -7.413458e-05 -8.632519e-05 -9.696531e-05
#> [541] -1.076196e-04 -1.150165e-04 -1.217164e-04 -1.233522e-04 -1.257278e-04
#> [546] -1.229397e-04 -1.170649e-04 -1.080322e-04 -9.848736e-05 -9.006630e-05
#> [551] -8.178750e-05 -7.318152e-05 -6.588423e-05 -6.423416e-05 -7.100514e-05
#> [556] -8.312463e-05 -9.641054e-05 -1.071929e-04 -1.171218e-04 -1.245898e-04
#> [561] -1.316879e-04 -1.383451e-04 -1.418302e-04 -1.426410e-04 -1.391275e-04
#> [566] -1.344333e-04 -1.290279e-04 -1.228544e-04 -1.158842e-04 -1.075343e-04
#> [571] -9.934084e-05 -9.072064e-05 -8.429105e-05 -8.000940e-05 -7.621140e-05
#> [576] -7.498807e-05 -7.470357e-05 -7.587000e-05 -7.636787e-05 -7.724980e-05
#> [581] -7.584155e-05 -7.535791e-05 -7.582733e-05 -7.939774e-05 -8.392121e-05
#> [586] -8.938352e-05 -9.683729e-05 -1.068088e-04 -1.192839e-04 -1.336936e-04
#> [591] -1.489283e-04 -1.649596e-04 -1.818017e-04 -2.006353e-04 -2.216453e-04
#> [596] -2.418017e-04 -2.600663e-04 -2.768657e-04 -2.915599e-04 -3.078188e-04
#> [601] -3.218728e-04 -3.320720e-04 -3.389283e-04 -3.451303e-04 -3.527975e-04
#> [606] -3.592128e-04 -3.670080e-04 -3.735514e-04 -3.810051e-04 -3.877477e-04
#> [611] -3.980179e-04 -4.091132e-04 -4.220578e-04 -4.346324e-04 -4.504646e-04
#> [616] -4.684020e-04 -4.889141e-04 -5.098529e-04 -5.290990e-04 -5.487861e-04
#> [621] -5.667519e-04 -5.855997e-04 -6.052726e-04 -6.230677e-04 -6.395115e-04
#> [626] -6.540208e-04 -6.712896e-04 -6.890848e-04 -7.094831e-04 -7.292982e-04
#> [631] -7.496111e-04 -7.666808e-04 -7.799383e-04 -7.914461e-04 -8.006353e-04
#> [636] -8.096253e-04 -8.179041e-04 -8.254575e-04 -8.306353e-04 -8.334091e-04
#> [641] -8.337790e-04 -8.354859e-04 -8.358558e-04 -8.364674e-04 -8.346609e-04
#> [646] -8.331389e-04 -8.297960e-04 -8.284020e-04 -8.284447e-04 -8.320578e-04
#> [651] -8.370933e-04 -8.446182e-04 -8.514319e-04 -8.569938e-04 -8.618444e-04
#> [656] -8.638501e-04 -8.661829e-04 -8.693266e-04 -8.743195e-04 -8.795684e-04
#> [661] -8.840208e-04 -8.892982e-04 -8.943622e-04 -9.019013e-04 -9.119155e-04
#> [666] -9.228117e-04 -9.330108e-04 -9.425130e-04 -9.491275e-04 -9.511901e-04
#> [671] -9.475770e-04 -9.446467e-04 -9.423992e-04 -9.403081e-04 -9.352299e-04
#> [676] -9.277619e-04 -9.189568e-04 -9.076908e-04 -8.944475e-04 -8.808487e-04
#> [681] -8.648174e-04 -8.485300e-04 -8.261403e-04 -8.035371e-04 -7.791417e-04
#> [686] -7.556709e-04 -7.315884e-04 -7.074916e-04 -6.850450e-04 -6.632811e-04
#> [691] -6.428544e-04 -6.233238e-04 -6.036652e-04 -5.849027e-04 -5.680464e-04
#> [696] -5.535798e-04 -5.420151e-04 -5.333522e-04 -5.249027e-04 -5.145187e-04
#> [701] -5.033095e-04 -4.935229e-04 -4.847462e-04 -4.770649e-04 -4.688999e-04
#> [706] -4.628970e-04 -4.553010e-04 -4.471645e-04 -4.379895e-04 -4.293835e-04
#> [711] -4.213323e-04 -4.117022e-04 -4.031815e-04 -3.931389e-04 -3.831815e-04
#> [716] -3.727406e-04 -3.649881e-04 -3.561972e-04 -3.464248e-04 -3.341061e-04
#> [721] -3.213750e-04 -3.071645e-04 -2.936083e-04 -2.807349e-04 -2.707206e-04
#> [726] -2.604361e-04 -2.520720e-04 -2.451019e-04 -2.384447e-04 -2.320862e-04
#> [731] -2.228401e-04 -2.150165e-04 -2.091986e-04 -2.080606e-04 -2.094262e-04
#> [736] -2.121858e-04 -2.157847e-04 -2.202086e-04 -2.259980e-04 -2.326837e-04
#> [741] -2.382029e-04 -2.420720e-04 -2.464532e-04 -2.492697e-04 -2.495115e-04
#> [746] -2.477334e-04 -2.461118e-04 -2.441773e-04 -2.419866e-04 -2.373921e-04
#> [751] -2.319440e-04 -2.288288e-04 -2.243480e-04 -2.190990e-04 -2.115172e-04
#> [756] -2.021431e-04 -1.925699e-04 -1.811616e-04 -1.694831e-04 -1.575343e-04
#> [761] -1.442484e-04 -1.296396e-04 -1.137078e-04 -9.753430e-05 -7.951154e-05
#> [766] -6.278323e-05 -4.517299e-05 -3.195820e-05 -2.099091e-05 -1.495962e-05
#> [771] -1.333800e-05 -1.612605e-05 -2.120428e-05 -2.754852e-05 -3.409191e-05
#> [776] -4.235649e-05 -5.063529e-05 -5.780457e-05 -6.332377e-05 -6.670926e-05
#> [781] -6.965379e-05 -7.063529e-05 -7.023700e-05 -6.582733e-05 -6.015165e-05
#> [786] -5.382164e-05 -4.633942e-05 -3.878608e-05 -3.062107e-05 -2.659546e-05
#> [791] -2.770500e-05 -3.441908e-05 -4.293970e-05 -5.262676e-05 -6.390699e-05
#> [796] -7.670926e-05 -8.992406e-05 -1.045898e-04 -1.206637e-04 -1.396965e-04
#> [801] -1.615741e-04 -1.840777e-04 -2.066950e-04 -2.257704e-04 -2.429966e-04
#> [806] -2.546893e-04 -2.646182e-04 -2.696680e-04 -2.747889e-04 -2.774916e-04
#> [811] -2.811047e-04 -2.809340e-04 -2.802939e-04 -2.766097e-04 -2.725841e-04
#> [816] -2.665955e-04 -2.586296e-04 -2.487150e-04 -2.357989e-04 -2.203935e-04
#> [821] -2.051303e-04 -1.878472e-04 -1.706495e-04 -1.508487e-04 -1.295542e-04
#> [826] -1.062967e-04 -8.433373e-05 -6.309618e-05 -4.301083e-05 -2.608337e-05
#> [831] -1.111894e-05 3.746634e-07 9.535403e-06 1.589387e-05 1.953540e-05
#> [836] 2.319116e-05 2.744436e-05 3.232345e-05 3.890951e-05 4.560937e-05
#> [841] 5.238035e-05 5.865347e-05 6.329074e-05 6.834052e-05 7.108590e-05
#> [846] 7.146997e-05 6.890951e-05 6.761506e-05 6.754394e-05 6.811293e-05
#> [851] 6.550980e-05 6.178291e-05 5.848277e-05 5.613569e-05 5.427225e-05
#> [856] 5.351833e-05 5.341876e-05 5.509728e-05 5.703185e-05 6.146997e-05
#> [861] 6.582274e-05 7.279287e-05 7.866769e-05 8.454252e-05 8.559515e-05
#> [866] 8.334764e-05 7.610724e-05 6.647708e-05 5.068761e-05 3.348988e-05
#> [871] 1.216698e-05 -1.009475e-05 -3.807484e-05 -6.855848e-05 -1.052868e-04
#> [876] -1.514603e-04 -2.038216e-04
#>
#> $MO2_fish4$`Starting Times`
#> [1] 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58
#> [16] 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88
#> [31] 90 92 94 96 98 100 102 104 106 108 110 112 114 116 118
#> [46] 120 122 124 126 128 130 132 134 136 138 140 142 144 146 148
#> [61] 150 152 154 156 158 160 162 164 166 168 170 172 174 176 178
#> [76] 180 182 184 186 188 190 192 194 196 198 200 202 204 206 208
#> [91] 210 212 214 216 218 220 222 224 226 228 230 232 234 236 238
#> [106] 240 242 244 246 248 250 252 254 256 258 260 262 264 266 268
#> [121] 270 272 274 276 278 280 282 284 286 288 290 292 294 296 298
#> [136] 300 302 304 306 308 310 312 314 316 318 320 322 324 326 328
#> [151] 330 332 334 336 338 340 342 344 346 348 350 352 354 356 358
#> [166] 360 362 364 366 368 370 372 374 376 378 380 382 384 386 388
#> [181] 390 392 394 396 398 400 402 404 406 408 410 412 414 416 418
#> [196] 420 422 424 426 428 430 432 434 436 438 440 442 444 446 448
#> [211] 450 452 454 456 458 460 462 464 466 468 470 472 474 476 478
#> [226] 480 482 484 486 488 490 492 494 496 498 500 502 504 506 508
#> [241] 510 512 514 516 518 520 522 524 526 528 530 532 534 536 538
#> [256] 540 542 544 546 548 550 552 554 556 558 560 562 564 566 568
#> [271] 570 572 574 576 578 580 582 584 586 588 590 592 594 596 598
#> [286] 600 602 604 606 608 610 612 614 616 618 620 622 624 626 628
#> [301] 630 632 634 636 638 640 642 644 646 648 650 652 654 656 658
#> [316] 660 662 664 666 668 670 672 674 676 678 680 682 684 686 688
#> [331] 690 692 694 696 698 700 702 704 706 708 710 712 714 716 718
#> [346] 720 722 724 726 728 730 732 734 736 738 740 742 744 746 748
#> [361] 750 752 754 756 758 760 762 764 766 768 770 772 774 776 778
#> [376] 780 782 784 786 788 790 792 794 796 798 800 802 804 806 808
#> [391] 810 812 814 816 818 820 822 824 826 828 830 832 834 836 838
#> [406] 840 842 844 846 848 850 852 854 856 858 860 862 864 866 868
#> [421] 870 872 874 876 878 880 882 884 886 888 890 892 894 896 898
#> [436] 900 902 904 906 908 910 912 914 916 918 920 922 924 926 928
#> [451] 930 932 934 936 938 940 942 944 946 948 950 952 954 956 958
#> [466] 960 962 964 966 968 970 972 974 976 978 980 982 984 986 988
#> [481] 990 992 994 996 998 1000 1002 1004 1006 1008 1010 1012 1014 1016 1018
#> [496] 1020 1022 1024 1026 1028 1030 1032 1034 1036 1038 1040 1042 1044 1046 1048
#> [511] 1050 1052 1054 1056 1058 1060 1062 1064 1066 1068 1070 1072 1074 1076 1078
#> [526] 1080 1082 1084 1086 1088 1090 1092 1094 1096 1098 1100 1102 1104 1106 1108
#> [541] 1110 1112 1114 1116 1118 1120 1122 1124 1126 1128 1130 1132 1134 1136 1138
#> [556] 1140 1142 1144 1146 1148 1150 1152 1154 1156 1158 1160 1162 1164 1166 1168
#> [571] 1170 1172 1174 1176 1178 1180 1182 1184 1186 1188 1190 1192 1194 1196 1198
#> [586] 1200 1202 1204 1206 1208 1210 1212 1214 1216 1218 1220 1222 1224 1226 1228
#> [601] 1230 1232 1234 1236 1238 1240 1242 1244 1246 1248 1250 1252 1254 1256 1258
#> [616] 1260 1262 1264 1266 1268 1270 1272 1274 1276 1278 1280 1282 1284 1286 1288
#> [631] 1290 1292 1294 1296 1298 1300 1302 1304 1306 1308 1310 1312 1314 1316 1318
#> [646] 1320 1322 1324 1326 1328 1330 1332 1334 1336 1338 1340 1342 1344 1346 1348
#> [661] 1350 1352 1354 1356 1358 1360 1362 1364 1366 1368 1370 1372 1374 1376 1378
#> [676] 1380 1382 1384 1386 1388 1390 1392 1394 1396 1398 1400 1402 1404 1406 1408
#> [691] 1410 1412 1414 1416 1418 1420 1422 1424 1426 1428 1430 1432 1434 1436 1438
#> [706] 1440 1442 1444 1446 1448 1450 1452 1454 1456 1458 1460 1462 1464 1466 1468
#> [721] 1470 1472 1474 1476 1478 1480 1482 1484 1486 1488 1490 1492 1494 1496 1498
#> [736] 1500 1502 1504 1506 1508 1510 1512 1514 1516 1518 1520 1522 1524 1526 1528
#> [751] 1530 1532 1534 1536 1538 1540 1542 1544 1546 1548 1550 1552 1554 1556 1558
#> [766] 1560 1562 1564 1566 1568 1570 1572 1574 1576 1578 1580 1582 1584 1586 1588
#> [781] 1590 1592 1594 1596 1598 1600 1602 1604 1606 1608 1610 1612 1614 1616 1618
#> [796] 1620 1622 1624 1626 1628 1630 1632 1634 1636 1638 1640 1642 1644 1646 1648
#> [811] 1650 1652 1654 1656 1658 1660 1662 1664 1666 1668 1670 1672 1674 1676 1678
#> [826] 1680 1682 1684 1686 1688 1690 1692 1694 1696 1698 1700 1702 1704 1706 1708
#> [841] 1710 1712 1714 1716 1718 1720 1722 1724 1726 1728 1730 1732 1734 1736 1738
#> [856] 1740 1742 1744 1746 1748 1750 1752 1754 1756 1758 1760 1762 1764 1766 1768
#> [871] 1770 1772 1774 1776 1778 1780 1782
#>
#> $MO2_fish4$`Minimum Adjusted Slope`
#> [1] -0.003663381
#>
#> $MO2_fish4$`Percentage of Fish Slope (%)`
#> [1] 1.055908
#>
#> $MO2_fish4$`Start Time for Minimum Slope`
#> [1] 174
#>
#> $MO2_fish4$`R-Squared for Minimum Slope`
#> [1] 0.9432022
#>
#> $MO2_fish4$`Mean R-Squared for all windows`
#> [1] 0.7281124Executing this function will return the following data for all fish in the file (up to a max of four individuals):
Minimum Adjusted Slope (mg O2/L/s): this is the steepest decline in water oxygen content measured using the rolling window throughout the dataset, after adjusting for the background change in oxygen content.
Blank Percentage of Fish Slope (%): The magnitude of the blank slope, expressed as a percentage of the minimum adjusted slope.
Start Time for Minimum Slope (s): The time at which the minimum slope started.
R-Squared for Minimum Slope: the R-squared value of the minimum slope.
Mean R-Squared for all windows: mean r-squared for all slope in the dataset.
Plotting the Slopes
The function “plot_slopes” returns plots of all slopes vs time, and the r-squared values for each slope vs time (for all fish separately). These measures can provide additional insight into the data for our four fish.
For example, we previously suspected that fish 4 had data of insufficient quality. However, the plot of r-squared vs time for this fish shows us that, around the time at which the minimum slope occurred for this individual, slope r-squared values were high. Indeed, the slope for the minimum slope was 0.96 and is therefore of sufficient quality.
“plot_slopes” contains the same arguments as the function “slopes”.
#> TableGrob (2 x 2) "arrange": 4 grobs
#> z cells name grob
#> MO2_fish1 1 (1-1,1-1) arrange gtable[layout]
#> MO2_fish2 2 (1-1,2-2) arrange gtable[layout]
#> MO2_fish3 3 (2-2,1-1) arrange gtable[layout]
#> MO2_fish4 4 (2-2,2-2) arrange gtable[layout]
#> TableGrob (2 x 2) "arrange": 4 grobs
#> z cells name grob
#> MO2_fish1 1 (1-1,1-1) arrange gtable[layout]
#> MO2_fish2 2 (1-1,2-2) arrange gtable[layout]
#> MO2_fish3 3 (2-2,1-1) arrange gtable[layout]
#> MO2_fish4 4 (2-2,2-2) arrange gtable[layout]Calculating MMR and Other Variables
After calculating all of our slopes with the “slopes” function, we can calculate our main variables of interest using the “calculate_variables” function.
Before running “calculate_variables”, you will need to run the following script to supply to values required for the calculations:
# Example input values for each fish column, ordered MO2_fish1, MO2_fish2, MO2_fish3, MO2_fish4
tubing_diameters <- c(0.03, 0.03, 0.03, 0.03) # diameters of the tubing in the mixing circuits, in cm
tubing_lengths <- c(50, 50, 50, 50) # lengths of the tubing in the mixing circuit, in cm
V_chambers <- c(50, 50, 50, 50) # volume of the respirometry chambers, in mL
fish_masses <- c(1.429, 0.924, 0.879, 1.324) # wet masses of the fish, in grams
smr_values <- c(0.056, 0.095, 0.124, 0.041) # SMR values for each fish, mg O2/hValues for the tubing diameters and lengths are used by the function to calculate the volume of the tubing, using the formula for the volume of a cylinder. This is then added to the volume of the respirometry chambers. The mass of the fish is then subtracted from the combined volume of the tubing plus the chamber, assuming that 1 gram of fish equals about 1 mL volume, to adjust the total volume of the system for the volume taken up by the fish.
The slopes measured by the “slopes” function are then multiplied by the resulting system volume and 3600 s/h, to give rates of oxygen uptake for each slope (in mg O2/h).
As an optional step, you can add the standard metabolic rate values for each fish. This will allow the function to calculate the integrated excess-post exercise oxygen consumption throughout the initial recovery period, as a means of estimating EPOC (in mg O2). This value is calculated as the area under the curve of oxygen uptake versus time, using the trapezoid rule, with the entered SMR values acting as the “floor” for these calculations.
The “calculate_variables” also includes the same data, exclude_time, and window_duration arguments as the “slopes” function.
calculate_variables(data, tubing_diameters, tubing_lengths, V_chambers, fish_masses, exclude_time = 30, window_duration = 150, smr_values)
#> Maximum Metabolic Rate for MO2_fish1 : 0.5481385 mg O2/h
#> Total Volume for MO2_fish1 : 48.60634 mL
#> Maximum Metabolic Rate for MO2_fish2 : 0.7476655 mg O2/h
#> Total Volume for MO2_fish2 : 49.11134 mL
#> Maximum Metabolic Rate for MO2_fish3 : 1.029754 mg O2/h
#> Total Volume for MO2_fish3 : 49.15634 mL
#> Maximum Metabolic Rate for MO2_fish4 : 0.6424135 mg O2/h
#> Total Volume for MO2_fish4 : 48.71134 mL
#>
#> Summary for MO2_fish1 :
#> Maximum Metabolic Rate (mg O2/h): 0.5481385
#> R-Squared for MMR:
#> Time for MMR (s): 122
#> Exponential Rate of Recovery (1/s): 0.002203013
#> Asymptote (mg O2/h): 0.1310775
#> EPOC (mg O2): 328.2726
#>
#> Summary for MO2_fish2 :
#> Maximum Metabolic Rate (mg O2/h): 0.7476655
#> R-Squared for MMR:
#> Time for MMR (s): 86
#> Exponential Rate of Recovery (1/s): 0.002984387
#> Asymptote (mg O2/h): 0.1422537
#> EPOC (mg O2): 269.4318
#>
#> Summary for MO2_fish3 :
#> Maximum Metabolic Rate (mg O2/h): 1.029754
#> R-Squared for MMR:
#> Time for MMR (s): 126
#> Exponential Rate of Recovery (1/s): 0.002962571
#> Asymptote (mg O2/h): 0.1256968
#> EPOC (mg O2): 319.1238
#>
#> Summary for MO2_fish4 :
#> Maximum Metabolic Rate (mg O2/h): 0.6424135
#> R-Squared for MMR:
#> Time for MMR (s): 174
#> Exponential Rate of Recovery (1/s): 0.001011217
#> Asymptote (mg O2/h): -0.1089595
#> EPOC (mg O2): 263.1955For each fish, this function returns the following data:
Maximum Metabolic Rate (mg O2/h): calculated from the steepest (minimum) slope identified in the dataset. R-Squared for MMR: The r-squared value of the slope used to calculate MMR. Time for MMR (s): The time at which MMR occurred.
The function also applies an exponential decay model to estimate the following parameters:
Exponential Rate of Recovery (1/s): The rate of decline in metabolic rate over time, as the fish recovers post-exercise. Asymptote (mg O2/h): The asymptote of the model. Theoretically this may approximate SMR or routine metabolic rate (RMR), but may be difficult to estimate accurately over relaitvely short timescales.
If SMR values were supplied, the values for EPOC over the time frame in the data are also returned.
Finally, plots of MO2 vs time are also shown for each fish, with MMR and the exponential decay model overlaid. Plots showing the areas under the curve, that were used to estimate EPOC are also displayed, with SMR shown as floor of these areas (the black dashed lines).