All 2020-05-07
## Warning: package 'rmarkdown' was built under R version 3.6.3
## Warning: package 'viridis' was built under R version 3.6.3
SAMPLING: We decided to use the the NIWOT saddle grid data to answer our questions. This grid was sampled intermittently since 1989 and annually or biannually since 2006. How often is each year represented? We first wanted to know how sampling differed across the years in the data set. Based on this information we excluded 1996 from our analysis
##
## 1989 1990 1995 1996 1997 2006 2008 2010 2011 2012 2013 2014
## 9600 11771 12249 195 11535 13274 15521 15513 15801 15320 15641 15919
## 2015 2016 2017 2018
## 16948 17117 17299 17473
we also noticed sampling design seemed to vary. In some years middle hits were taken and in others they were not. In some years top hits were recorded for every plot but not in others.
Ultimately we decide to isolate only the top hits and covert bottom only hits to top hits in the early years
SPECIES TRENDS: Although intially we were primarily interested in the Deschapsia-Geum interaction we noticed that kobresia myoseroides was the second most common hit at decided to include it in our analysis.
We next wanted to visualise how each species was changing throughout time. Was Deschampsia becoming much more abundant across the whole site?
Within certain habitat type it seems like relative abundance of certain species changes
Elevation 1 with veg classes
SNOW DEPTH: early models showed that that there was a lot of variation in space, with somewhat less in time.The only explanatory variable we had that varied in both space and time was snow depth. Snow depth was measured at stakes associated with each plot starting in March on a biweekly (mostly) schedule, producing a mountain of data.
We felt the most biologically relavant data to be max depth and snow melt date. Due to gaps in the data it was hard to estimate snow melt date reliably. Scott tried valiantly to fit GAMs to the snow data to allow us to estimate snow melts. But after several weeks and many discussions the GAMs grew too frustrating and we opted to use mean snow depth in June - simpler metric that still included some of that information.
We were able use temperature data at the site level from the saddle data logger. This provided us with a wealth of data at a fine temporal scale.
However, we decided to use the summer mean daily maximum from June - August as our predictor. In different models we used both current year, and the previous three years seperately.
SPATIAL AUTOCORELATION: We also included a spatial autocorrelation term to incorporate the effect of the abuncance of each speces in the previous year weighted by distance to the plot.
## 1 2 3 4 5 6
## 1 NA 0.018605375 0.009554163 0.006517692 0.004939944 0.004062494
## 2 0.018605375 NA 0.019632046 0.010030774 0.006725057 0.005196821
## 3 0.009554163 0.019632046 NA 0.020419583 0.010212295 0.007059780
## 4 0.006517692 0.010030774 0.020419583 NA 0.020406318 0.010783620
## 5 0.004939944 0.006725057 0.010212295 0.020406318 NA 0.022866823
## 6 0.004062494 0.005196821 0.007059780 0.010783620 0.022866823 NA
## 7 0.003370893 0.004116174 0.005202918 0.006980081 0.010606854 0.019778053
## 8 0.002827359 0.003333498 0.004011909 0.004992096 0.006607771 0.009291998
## 9 0.002513205 0.002905238 0.003407239 0.004089197 0.005113192 0.006585127
## 10 0.002213796 0.002512565 0.002879925 0.003352160 0.004010943 0.004864126
## 7 8 9 10 11 12
## 1 0.003370893 0.002827359 0.002513205 0.002213796 0.020111952 0.013632434
## 2 0.004116174 0.003333498 0.002905238 0.002512565 0.013361892 0.021458996
## 3 0.005202918 0.004011909 0.003407239 0.002879925 0.008542482 0.015278266
## 4 0.006980081 0.004992096 0.004089197 0.003352160 0.006089983 0.009219438
## 5 0.010606854 0.006607771 0.005113192 0.004010943 0.004727268 0.006486704
## 6 0.019778053 0.009291998 0.006585127 0.004864126 0.003930252 0.005090444
## 7 NA 0.017525619 0.009871987 0.006449279 0.003283393 0.004060797
## 8 0.017525619 NA 0.022604965 0.010199742 0.002768339 0.003303522
## 9 0.009871987 0.022604965 NA 0.018540678 0.002467517 0.002884783
## 10 0.006449279 0.010199742 0.018540678 NA 0.002181938 0.002502956
## 13 14 15 16 17 18
## 1 0.008642535 0.006380985 0.004780996 0.003941439 0.003270848 0.002855587
## 2 0.014135723 0.009355845 0.006362664 0.004981289 0.003958862 0.003369010
## 3 0.021283542 0.015561684 0.009199009 0.006638861 0.004945854 0.004062669
## 4 0.014118368 0.019525083 0.014248395 0.009460919 0.006412413 0.005026434
## 5 0.009013797 0.013384855 0.020040418 0.015543836 0.009055335 0.006591843
## 6 0.006603028 0.009000350 0.015010747 0.022493221 0.013488316 0.008999216
## 7 0.004990514 0.006311975 0.009273131 0.015077949 0.019257377 0.014201978
## 8 0.003901983 0.004683458 0.006221081 0.008693973 0.013329861 0.019209997
## 9 0.003333691 0.003892870 0.004916644 0.006377431 0.008974730 0.013506969
## 10 0.002837047 0.003236204 0.003924077 0.004814235 0.006280664 0.008590933
## 19 20 21 22 23 24
## 1 0.002473949 0.002199567 0.010214867 0.008866315 0.006829080 0.005399033
## 2 0.002851385 0.002493353 0.009355327 0.010055010 0.008867951 0.007080310
## 3 0.003334524 0.002855510 0.007427582 0.009068785 0.010312827 0.009396767
## 4 0.003965634 0.003308728 0.005711289 0.006989265 0.008994907 0.010473709
## 5 0.004898704 0.003938700 0.004593455 0.005508596 0.007167094 0.009502373
## 6 0.006174446 0.004735736 0.003877513 0.004551028 0.005773631 0.007668570
## 7 0.008663972 0.006135455 0.003267002 0.003750404 0.004599109 0.005876993
## 8 0.014367103 0.009012311 0.002768987 0.003117463 0.003705561 0.004541282
## 9 0.018940500 0.013154682 0.002473552 0.002751467 0.003207864 0.003829865
## 10 0.014166138 0.019944504 0.002193673 0.002412875 0.002763547 0.003222333
## 25 26 27 28 29 30
## 1 0.004435161 0.003675549 0.003188457 0.002738414 0.002425225 0.002160707
## 2 0.005633526 0.004501569 0.003807276 0.003191552 0.002779033 0.002438452
## 3 0.007414653 0.005683001 0.004655054 0.003783794 0.003225681 0.002777729
## 4 0.009345514 0.007182761 0.005744889 0.004530557 0.003777997 0.003184518
## 5 0.010616256 0.009152933 0.007339130 0.005617237 0.004557922 0.003734731
## 6 0.009643154 0.010272935 0.009023888 0.006953160 0.005525868 0.004391181
## 7 0.007449096 0.009230546 0.009936376 0.008744966 0.007059244 0.005418802
## 8 0.005566280 0.007012435 0.008529696 0.009743522 0.009205543 0.007070575
## 9 0.004570666 0.005630161 0.006885191 0.008733897 0.010031577 0.008557056
## 10 0.003752374 0.004502106 0.005410102 0.007007117 0.009189548 0.009971596
## 31 32 33 34 35 36
## 1 0.006520436 0.006359163 0.005334040 0.004602667 0.004036932 0.003404223
## 2 0.006227661 0.006765081 0.006196479 0.005498134 0.004858509 0.004034046
## 3 0.005532633 0.006470295 0.006715356 0.006393609 0.005856040 0.004838514
## 4 0.004657679 0.005538594 0.006291024 0.006596158 0.006566982 0.005640032
## 5 0.003972718 0.004705493 0.005575673 0.006286715 0.006893421 0.006459635
## 6 0.003470534 0.004061311 0.004846123 0.005623012 0.006540141 0.006812343
## 7 0.003001093 0.003455108 0.004085726 0.004755023 0.005648003 0.006450704
## 8 0.002593770 0.002937497 0.003420803 0.003939119 0.004645150 0.005518627
## 9 0.002341641 0.002622825 0.003017276 0.003436045 0.003998534 0.004754550
## 10 0.002098945 0.002325973 0.002643854 0.002976284 0.003412545 0.004025794
## 37 38 39 40 41 42
## 1 0.002964050 0.002637831 0.002334785 0.002095821 0.004899521 0.004908893
## 2 0.003455798 0.003034438 0.002647363 0.002348306 0.004826727 0.005112710
## 3 0.004081445 0.003532120 0.003030478 0.002651087 0.004532801 0.005014829
## 4 0.004767770 0.004098714 0.003468382 0.002995281 0.004027326 0.004536812
## 5 0.005613247 0.004840298 0.004044420 0.003442250 0.003577329 0.004050827
## 6 0.006317744 0.005598725 0.004674782 0.003937760 0.003207876 0.003620652
## 7 0.006649709 0.006359826 0.005481919 0.004623582 0.002832580 0.003171029
## 8 0.006218565 0.006649073 0.006301481 0.005523198 0.002487778 0.002757072
## 9 0.005508705 0.006254133 0.006524755 0.006141772 0.002265582 0.002491856
## 10 0.004705914 0.005526679 0.006324744 0.006674668 0.002048606 0.002236043
## 43 44 45 46 47 48
## 1 0.004425288 0.004008199 0.003490267 0.003081101 0.002787610 0.002464506
## 2 0.004842840 0.004555425 0.004013442 0.003529459 0.003180569 0.002784533
## 3 0.005049435 0.005033841 0.004577282 0.004044769 0.003646618 0.003165557
## 4 0.004801262 0.005099422 0.004920262 0.004465950 0.004087733 0.003554606
## 5 0.004427030 0.004941605 0.005104787 0.004844773 0.004566657 0.004018355
## 6 0.004010674 0.004588621 0.004995392 0.004987413 0.004899681 0.004432901
## 7 0.003524409 0.004065577 0.004589015 0.004828569 0.005010411 0.004789486
## 8 0.003054879 0.003510721 0.004022446 0.004390622 0.004782045 0.004920263
## 9 0.002748004 0.003135138 0.003591802 0.003969960 0.004412543 0.004759374
## 10 0.002453166 0.002775038 0.003167552 0.003524027 0.003963335 0.004445728
## 49 50 51 52 53 54
## 1 0.002231060 0.002021729 0.004045189 0.003963762 0.003704233 0.003407525
## 2 0.002500469 0.002247231 0.003980800 0.004078191 0.003975229 0.003727576
## 3 0.002819788 0.002511880 0.003796697 0.004042934 0.004131461 0.003985801
## 4 0.003157790 0.002797235 0.003462379 0.003774175 0.004013375 0.004005097
## 5 0.003575069 0.003153319 0.003151603 0.003482943 0.003813198 0.003925767
## 6 0.003987726 0.003522856 0.002881190 0.003198803 0.003555053 0.003738634
## 7 0.004445587 0.003986633 0.002591189 0.002874621 0.003215799 0.003432589
## 8 0.004840448 0.004520201 0.002312579 0.002554819 0.002857788 0.003074157
## 9 0.004929419 0.004838394 0.002126613 0.002338895 0.002607833 0.002809454
## 10 0.004861615 0.005116755 0.001941969 0.002125628 0.002360481 0.002543742
## 55 56 57 58 59 60
## 1 0.003136640 0.002783751 0.002551218 0.002319066 0.002112378 0.001935119
## 2 0.003496528 0.003106557 0.002850904 0.002580207 0.002339209 0.002130392
## 3 0.003851476 0.003451705 0.003186738 0.002877403 0.002599213 0.002353542
## 4 0.004030179 0.003699158 0.003473975 0.003154044 0.002855766 0.002579729
## 5 0.004120335 0.003911958 0.003769173 0.003463170 0.003156959 0.002850200
## 6 0.004052110 0.003989452 0.003963939 0.003714852 0.003433308 0.003112309
## 7 0.003811762 0.003905390 0.004033391 0.003907822 0.003712723 0.003411653
## 8 0.003456998 0.003662457 0.003924703 0.003968178 0.003935087 0.003719914
## 9 0.003165009 0.003406766 0.003720611 0.003874397 0.003982651 0.003882829
## 10 0.002863053 0.003120194 0.003458188 0.003704965 0.003963618 0.004030178
## 61 62 63 64 65 66
## 1 0.003283962 0.003274559 0.003196865 0.002921626 0.002776659 0.002523825
## 2 0.003269433 0.003337371 0.003354774 0.003128522 0.003011737 0.002758672
## 3 0.003186311 0.003321027 0.003439269 0.003294019 0.003227150 0.002996604
## 4 0.002988009 0.003158610 0.003351324 0.003309794 0.003314288 0.003149385
## 5 0.002791263 0.002979504 0.003220742 0.003275431 0.003354442 0.003277997
## 6 0.002604711 0.002793182 0.003051086 0.003172045 0.003307897 0.003322317
## 7 0.002389025 0.002565619 0.002817867 0.002983600 0.003160640 0.003269265
## 8 0.002169503 0.002327595 0.002559050 0.002744037 0.002938696 0.003119518
## 9 0.002016141 0.002158809 0.002369443 0.002553612 0.002745822 0.002954398
## 10 0.001861094 0.001988240 0.002176998 0.002355663 0.002539863 0.002765052
## 67 68 69 70 71 72
## 1 0.002339794 0.002161059 0.001989951 0.001840479 0.002848376 0.002834228
## 2 0.002565216 0.002369641 0.002176553 0.002007046 0.002831541 0.002892995
## 3 0.002804843 0.002597765 0.002383215 0.002192585 0.002772439 0.002901956
## 4 0.002989213 0.002793920 0.002572795 0.002370410 0.002629552 0.002802303
## 5 0.003170181 0.003004014 0.002785845 0.002575946 0.002486997 0.002687252
## 6 0.003281272 0.003164913 0.002968652 0.002764799 0.002347809 0.002557125
## 7 0.003314311 0.003279994 0.003138703 0.002966360 0.002181161 0.002386418
## 8 0.003247690 0.003314306 0.003264104 0.003160468 0.002006060 0.002198014
## 9 0.003124831 0.003257685 0.003286106 0.003256417 0.001880165 0.002058365
## 10 0.002967838 0.003162270 0.003279897 0.003349641 0.001751208 0.001914803
## 73 74 75 76 77 78
## 1 0.002779654 0.002627397 0.002479483 0.002362790 0.002143989 0.002011097
## 2 0.002886590 0.002776593 0.002654268 0.002549103 0.002315944 0.002179328
## 3 0.002947303 0.002894222 0.002813245 0.002731219 0.002492249 0.002358061
## 4 0.002890582 0.002901982 0.002879154 0.002837576 0.002618055 0.002503235
## 5 0.002808349 0.002879236 0.002918112 0.002925144 0.002739580 0.002655501
## 6 0.002695328 0.002808044 0.002897754 0.002950894 0.002811984 0.002768623
## 7 0.002530643 0.002673165 0.002806472 0.002905704 0.002832629 0.002847760
## 8 0.002338623 0.002495605 0.002656883 0.002791609 0.002791162 0.002874230
## 9 0.002191384 0.002348853 0.002517676 0.002666155 0.002712009 0.002839791
## 10 0.002038900 0.002193650 0.002365301 0.002522539 0.002612578 0.002784608
## 79 80 101 201 301 401
## 1 0.001871677 0.001743387 0.001991793 0.001982628 0.001977997 0.001905514
## 2 0.002025652 0.001884286 0.002230397 0.002218705 0.002209069 0.002114794
## 3 0.002191534 0.002037657 0.002515174 0.002501327 0.002485055 0.002361184
## 4 0.002335099 0.002177523 0.002868054 0.002845126 0.002809251 0.002639722
## 5 0.002492043 0.002335095 0.003336976 0.003302433 0.003235023 0.002995758
## 6 0.002620932 0.002474097 0.003907140 0.003852347 0.003728473 0.003389557
## 7 0.002734790 0.002615330 0.004868392 0.004756076 0.004482725 0.003948799
## 8 0.002815898 0.002745879 0.006739585 0.006435220 0.005699848 0.004752535
## 9 0.002829117 0.002808095 0.009596580 0.008734286 0.006964944 0.005466948
## 10 0.002830330 0.002873767 0.019858679 0.015015277 0.009002195 0.006467223
## 501 601 701 801
## 1 0.001870649 0.001780947 0.001707354 0.001636074
## 2 0.002065509 0.001949237 0.001854575 0.001765264
## 3 0.002291782 0.002140295 0.002018411 0.001906596
## 4 0.002536964 0.002336719 0.002179220 0.002040098
## 5 0.002842010 0.002572819 0.002367323 0.002193144
## 6 0.003163085 0.002807899 0.002546495 0.002334230
## 7 0.003584801 0.003094031 0.002752168 0.002489673
## 8 0.004124336 0.003427827 0.002977032 0.002652977
## 9 0.004533910 0.003657626 0.003121454 0.002753832
## 10 0.005038867 0.003934616 0.003294951 0.002876926
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