Note for ECOL 406R with Professor Bonine at UA
Note for ECOL 406R with Professor Bonine at UA
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Date Created: 02/06/15
Conservation Lecture 19 24 Oct 2006 CH6 Genetics CH7 Populations Genmlcs Conservation Biology ECOL 406R506R University of Arizona Fall 2006 Kevin Bonine Kathy Gerst PVA etc Lab this week 7am 2729 7pm October ORPI Pinacate CEDO food see website for lab readings Housekeeping 24 October 2006 Upcoming Readings today Text Ch6 and 7 PVA Puma concoor Thurs 26 Oct Guy McPherson web for climate change reading Tues 31 Oct Ed Moll long web reading Thurs 02 Nov Exam Two Tues 07 Nov Don Falk web reading Thurs 09 Nov Conservation Practices Ch 10 Donlan EA 2005 Short oral presentations 24 Oct Cori and Robert 09 Nov Jon and Laura 14 Nov Dan and Lane 28 Nov Amanda and Fred 2 Global Climate Change Lecture Series All lectures will take place at UA Centennial Hall All ieeuuesbegmunpm andaxe rue in nu public CallSZEI 521 409 rumme urunuuuuu Tuesauy Ocmbex 17 Glabalclumle Change The Evldence mueum Hugues Mm erneumeumugy httpcosarizonaeduCumate Tuesauy OclnbexZA Glnbal Climate Change Whals Ahead imam Ovexpck Dmcmx af nu lnslmlle fax the Sludya laml Emu and meessax afGeascxences Tuesauy Oembem Glnbalchmalz Change The Rule afLrmg Thungs Trams anman Assistant meessax afEcalagyand Ewhmamxy Emlngy Tuesauy Nwembex 7 Glnbalchmalz Change Oceanlmpucls and Feedback luhacale Assacmlz meessaxafGeasciznces Tuesauy Nwembex 14 Glnbal Climate Change Disease andSacxely Andrew Cumue Deuu unue Gradual Callzge uua meessax af Geagnphyand Mum Develapmenl Tuesauy Nwembex 21 Glnbalchmalz Change Cauld Geaengmzenng Reveue m RaguAngel Ragenls meesmmfAslmnnmy Tuesauy Nwembex 22 Glabal Chmale Change Designmg Puhcszspanses m1 Pumuy Deuumue Ellszallege urMuuugemem and meessaxafEcannmms 3 Cori and Robert will speak for 10 minutes on xx Applications of Genetics to Conservation Biology MoecuarTaxonomy Populations Gene Flow Phylogeography Relatedness Paternity Individual ID Molecular Taxonomy Molecules versus Morphology Cryptic species sibling species Morphological variation without genetic variation Relatedness Kinship Paternity and Individual ID Application of molecular genetic techniques using hypervariable repetitive DNA ie microsatellites minisatellites to questions of kinship paternity or individual ID Populations Gene Flow Phylogeography Compare genetic traits among populations Resolve substructure among populations lnfer movement patterns among individuals lnfer historical events for species NonInvasive Sampling Allows sampling without disturbance to individual Rare or hard to capture species Examples hair scat feathers salivacheek swab regurgitated pellets dried blood biopsy dart museum tissues Subspecies Taxonomy Phylogeography Gene Flow Puma cougar mountain lion 32 Puma subspecies as of the early 1900s Objectives 0 Does current population differentiation re ect 7 Trinomial descriptions 7 Physical or ecological barriers 7 Isolation by distance 0 Are current levels of genetic variation the same Within each population 0 Does population structure and genetic variation re ect 7 Historic migrations 7 Historic dispersals 7 Historic bottlenecks Modern and museum puma samples collected total of 3 15 I Historic Current Contemporary Museum Molecular Methods Used Mitochondrial gene sequencing 7 l6SrRNA 7 NADH5 7 ATPaseS Nuclear microsatellite length determination 7 10 domestic cat microsatellite loci 14 Neutral Markers often studied Relevance to natural selection and adaptation Ultimately source of all variation is mutation mutation rate 10394 10396 15 Mitochondrial DNA Haplotypes in a geographical Cline n x a x i 5 u 1 1 s Mlnlmum Spannan Network 155Nn5m1a genes comhlnod Absoluu Number or Different Ancestral haplotypes 2 historical radiations Wm NA is most recently founded population sung um m k mm mm m m m unm u quot2 wwnmmsmo onka mmm Microsatellite Alleles at FCA008 HBH L39UEI 2 mum Evolution NeighbnrJoining Tree in Microsaieiiiies and 252 individuals Piaponion oi Shared Alleles distance Geographic clustering of individuals Norm Amerlca mm NSiX groups identi ed 2 distance methods agree Minlmum Evalution Neighborsloining Tree 10 Micmsaieimes and 29 Suhapecles Propanlon ol snared Allelzs dlstancl Subspecies associate into same 6 groups Statistical support from bootstrap values 2 distance methods agree Calculation of FStati comma umHHtmw Muvaleruwu FISI IITI IST mm mm or mum mm may by aLwVH wuqhx Hun we ma mm t man My Mum Haw gvnnmv man 0 mm g 5w mm vmn WWWMHW 1 Wmmhywhewunecattu quotXquot n pmrhrhMulwnuvnryuihmmoly WWWMMMW m m m hmkm n rm 4 nnuuvwl um mm MGMAmi m sullpuvuhnmws nn mum mm Imerqu dumnn mm mmquot WNW yumm 3 SHAWL m5 Immuva out 2 ame mm m nmqnunllhrohwrvrz mama n new m 7 u man mwam Am muv mm Huwmv Wm m Wm my wr vhwnvvnnnnm WWWW mumcamwuw quot quotquotquot quot Gmum Me e seam zuua mum mp mm mvtwmm m wfwlnuw V ight s Fst Estimates and Slatkin s h gration Estimates Fst near 0 little divergence nicmsatallitas 11 Summary 6 groups identi ed using microsatellites mtDNA haplotypes overlayed onto map supports 6 groups Location of 2 ancestral haplotypes Canlrgr America Monhem Major restrictions South America to gene ow Eastern Amazon River Southern Rio Parana X r CenIral R10 Negro 5 And 9 L America es Fossil Record versus Molecular Divergence Estimates Oldest fossils in North and South America date to 0203 Mya From mtDNA mutation rate of l 15 My divergence for extant puma lineages is 390000 years ago From mutation rate of 5 X 10399yr for microsatellite anking regions pumas are less than 230000 years old 12 Historical Inferences Extant pumas originated in Brazillian Highlands ancestral haplotypes Fossil record suggests dispersal to NA soon after the common origin in Brazil 2 historical radiation events occurred 25 Ancestor to puma crosses landbridge 23 Mya Puma origin in Brazilian Highlands 300000 ya l3 2 Maj or historical radiations One locally distributed One broad ranging quotmmquot Puma Radiations Puma Bottlenecks Subspecieslevel North America low overall genetic variation Populationlevel Florida monomorphic at 810 microsatellite loci Olympic Peninsula and Vancouver Island monomorphic at 510 microsatellite loci 28 14 Puma Conclusions Pumas originated in Brazil approximately 300000 years ago Possible extirpation and recolonization in North America Pleistocene age Molecular data does not support 32 subdivisions instead 6 groups Pumas are fairly panmictic Within 6 groups 29 Conservation Implications Maintain habitat connectivity within 6 large groups Management should consider effects of bottlenecked populations Eastem cougar Florida panther and Yuma puma management take into account revised subspecies 30 15 What is population Viability analysis PVA Thanks to Margaret Evans 2003 31 Populatlon m uimmim Dynamlcs mama Wm Dvmhmlv npuLlhuu mm mmwmn Whom lndividun MN MW ngm 121u i npuxanmdynanmsmum bu nndursmud us hitrumhy uipnm manning pnpuiannns 1 Landsmpmlmel changes in lhn 1 Iilabvhly m hnbxm dole VIN7P how mukh Mnmbic habitat musks fur a given sped and n mnnzumnon and ilwref ar hiliry The av Ianvlily o labia habit mil the WWW m physiology uf um um Urgdmam anbnw m mnuum 32 f uh 39 v quotm dwm Wquot m Gruum Me e e CarruH was 16 lemmings per hectare 40 27 populations are dynamic not static Lemmings Jaye f l l I 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 year Cause of cyclic change in population not completely understood Cycle length average 38 years Mass migration in response to high density with decreasing food supply sometimes swimming involved 33 populations are dynamic not static Whales in the Antarctic Fin whales Sei whales Index of abundance Blue whales 1945 6 1949 50 1959 60 1969 70 34 17 Population sizes change over time Why What causes change in population size What regulates population size If we can answer these questions we might be able to make changes that increase populations of declining endangered species 35 Many things affect population size competition population within a species structure am ong species other interactions predation herbivory pollination etc chance events gt popul ati on dem ographic Slze genetIc habitat attributes enVqunmental quantity quality succession or variatlon configuration and good years bad years connectivity disturbance 36 18 l Exponential growth densityindependent deterministic 239000 arithmetic In a closed population no immigration or emigration population growth is a function of birth and death rates 1000 Population size L N b39dN o 3938 3940 3942 t Ringnecked pheasant 37 on Protection Island exponential growth an unrealistic model Humans on planet Earth New Slant Agn Brom Ago Iron Age A3135 a 1970 w Pupulaliun billions 1930 soon 6000 40m 2000 e ac 14 2000 38 Years 19 2 Logistic growth densitydependent deterministic intraspecific competition dN rN dt K stabilizes population Size birth rates go down andor death rates go up with increasing population size ma carrying capacity K n m 20 an m an 64 70 w 9n mu mm 39 Alternatively The population growth rate may increase with population size positive densitydependence mm Allee effect Wm we nean minimum viable population size An 20 Allee effect 37 Fasscngur l lgrlm Lulnk nmluL How In animals group defense against predators group attack of prey mates difficult to nd critical number to stimulate breeding behavior In plants pollinator limitation selfincompatibility inbreeding depression Allee effect How group defense against predators 1 rm an Numuarntmeaonsmumb FIGURE 17 Success mu of goslmvk mucking pigeons in luck Mr R by mintd gmlmwk mm rmhcd 1n cupmrv nun 7 1 4 piguu from a huge the hung mm m ha The sage gm cemmm umphasranus a guumw w a m ingjc piguuns m muwm tbs warern wed 5mg gum rm mahng an mmmunu dwsp u and mm gmas mm a u Mme are msullmem b pm eUarmcmun dxsp uys and breedmg may mm plate L 21 The two categories of models we have considered thus far assume that all individuals in a population have the same birth and death rates no genetic developmental or physiological differences among individuals under some circumstances this might cause us to inaccurately predict population size 43 3 Structured population models densityindependent deterministic This is the type of model most often used in population viability analysis What is meant by structure A population is unstructured if all individuals have the same rates of survival and fertility A population is structured if differences among individuals in age developmental stage or size cause them to have different survival or fertility rates 44 22 TABLE 63 Survival data for redcockaded woodpeckers in different reproductive stages from Walters 1990 Fate at the end ofa Total number e39yea interval Proportion Stage of birdyears Dead Alive surviving one year Fledglings 616 345 271 044 Solitary males 131 50 81 062 Helpersatthenesl 273 60 213 078 Breeding males 838 201 637 076 Floaters 29 11 18 062 Life Tables 45 mm Table 7 i39n rum rnr lll liIIaK mum s quotinn 5 rrnmphilus hrldingil Lu mnm M u m mmml plm 1v xmm x l m Huinhlimi uluuwh uhu39 m u 1 m mum a w Hunt in n in mg m Mm u m Iruumm mwmm m n M u unmcmi w I m m m m leH m inkAmi u huh m in mm Llilmlaunm n I M um um I mu um I m u my I um i I I 273 In M um HJ Hm M u u H In um 47 7 3 n m HM is u x mm um um J 5 H H mm min 150 II M ulvlr v ml um 57 in m my m i li u um Im in as i u I 4 mm I nu I in I II 7 lt mm my V 1511 V 7 1 7 V 7 V 7 7 x a V 1 x I an I HTS V M if quot i um m7 ns 7 7 MN I mm l m NW 1 quotum I In 1 Wm 23 3 Densityindependent deterministic structured population growth What else can structured population models tell us Sensitivity The sensitivity of 7 to each matrix element describes how much 7 will be affected by a change in that transition probability Would it be better to focus conservation efforts on improving the survival of hatchlings or large juveniles or adults Lambda population growth rate 47 When lambda is greater than 1 the population increases in size When lambda is less than 1 the population decreases in size 48 24 3 Densityindependent deterministic structured population growth What else can structured population models tell us Elasticity Elasticities quantify the proportional change e g 1 in the asymptotic growth rate that can be expected given a particular change 1 in each life history transition 49 Van Dyke p 178 Four Horsemen of the Extinction Apocalypse 1 Genetic Stochasticity 2 Environmental Stochasticity 0quot Demographic Stochasticity 4 Natural Catastrophes 50 25 Population Viability Analysis mm A Potential LimoWA quotPmduzn axeqnyy of use Gumquot nmiumrmvni Spad K us Assessing iimcxmvrhun ream WI Mmiam Cmnpnnng widiiw nsLsm mu m mi popuialmns Anaiwmg and symlmswmg monmm ng dam Idenlllyng kc iitu Ingxsnrdumngmplnr pluth u mnlxngemulil my Ddalmnlng hm my 1 mens mas m be in W a mm icni m wmmmu rm vxlliminli Delvrm ming 1W m y mime L in ruimw m on i m pnpuiminn 5mm nmwn hu imrvuii m mm mm a populmum nu umvumpnlihicwilh ilscimummd mm Dmdms mm many WWW nn39 quotmm in mm a Mme mm mununi or zlniml Axllmiiun Souruslarexamples 5mm mnsmnwud Samson ms mm was Fammxnl was mum rmmimaom ii way Mungm and Gordon was Cwbrrcl m w amuse nl WW Slu u WM Arumw and mac was nus mum Huh 1 Inwalls and pm my um m7 Mnnmli mu rmmms um um 54mm 0 I my Nmhl n u mm Ihlsimmln m u mm Vli uulni 19W Gunoil I ma mm mm anmmnvur mui Maugham iwa Him I Jim Gmum Meffe e Carmii ZEIEIE 51 Ii nlulinn of Population l l39 bilit39 Awewmenle for the Florida 1 A Multipcrspoclhc Approach Ilrlrul S III In39 ivn r F Lue 1 If Naurle Lam mu Willmuv 539 II A inf IUILrz IN Population Viability Analysis SievenR BeissmgaandDaleR McCullough eds Univ ofchicago Press Chicago xvi 577 pp 52 Panther Article on PVAs over time VORTEX data population size source and sink inbreeding problems captive breeding introgression time scale HABITAT LOSS 53 1 ilrlu ill l mrrlml39rmn ul39ll39lT lml l luran l rmulrl luller ml rillu In mu I39Mv urlun rml llrr limpm rl lnmh39rl mi l lr mm M r H m qr r mu r rrr ml rum 1 Jim m l inlvujm mm mm rulmiimi r m rlvm n4 rrqmmm m r u lr llrrlwlrrrrrrllrlh Hi 7 7 7 rlru lrlrrir urkr mm m 7 7 7 llr rwrlrrr lrxru quirkll L m r H r r x q rm ll m ii er h Hmlw mm 39 l z m m Uri urrmrlm llrm J z 1 r r l r r I u u u u H w rlHle Mini w m 39Nr w in w m m urn r mmrrm m ur r 4 1 r A 39r ivrmlr wh lrrm r n 1 quotm M mi in rlYHr 1H 3 ill Ill 7 139 l739 in gun rim 4m mm 39m mm lm Juli mil 7 mm mm r 75 2 rr lllrl Irwmlr39 39wn u u run I my 3 u u 3i u wry m 1 mi rmrrlnlrh i may sir i lrrrr Hi 54 rum Munrer m u H J 39rrl 7 4r H 3n 27 m H mm mm M Nn v M u WNW u r m W Wm WM M Mum w 4 w W M7 7 mm m y WMHM m a u n n 1 m 7w w n u 7 rniumn m y 7 7 u no WWWquot w 7 7 7 am 7 WWW M m 7 7 w MI m y w m a y m m m a V m 1 n a w I a a u 4 u 7quot n n v m w h s 7 7 7 y i 39J mmx C w v mg 111 mi 7 m 7 WWW 39 39 n I z 7 quot mm m um um m m m 194 m um mnywu m an m M 4m h w W wwmmug H mm 13 212 WWW 4 2 W muw n 7 W WWW a hwth Minimum I 7 w l m 7 M a m u 7 3 Mum r 7 u Mmllq vl a A 1 7 7 MM mg a m E n 7 7w u 3 rmmm mu I quot mmquot L I 1572 7 quot w 4 m 41 lrmlwx Im u vIum 4 w quot maulquot o 339 mum h Tnhlx H 4 EIT T ul39 lnm39vming rn39lng nn Cumiv stern5 L39nsih39 Using Hu Inusum us VORTEX Slmulnliun l rmlirml llv h mngmm Liming Tulw39lh Tu Inn ISU Ndata 1933 1992 1935 c c u m w U xamn1unwl A W539Wu wh WWW mlwhwr l lquothrrllr lnrilnl39mmer am w 1 3 Der in Day is g as 3 as 9 j 7 t1me scale g D S n2 1 m 1 sn Votl 1502mm woman ssun Veer 397 m mm mm Mod n quotw mm m u m m vame mm 29 Last thoughts on PVA PVA requires lots of data which takes time work and money whereas managers want answers predictions about extinction now Few species will get thorough PVA When should PVA be used and what type of PVA how complex Predictions from PVA can only be as good as the data that go into the analysis We can only have degrees of con dence in the predictions from PVA Populations should not be managed to their minimum viable population size One of the greatest strengths of PVA is the ability to play what if games with the model That is what if management were to increase patch sizes or connectivity What if adult survival were improved 59 END 60 30
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