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by: Cade Harvey


Cade Harvey
GPA 3.74


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Class Notes
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Popular in Agriculture and Forestry

This 6 page Class Notes was uploaded by Cade Harvey on Friday September 18, 2015. The Class Notes belongs to ALS 5932 at University of Florida taught by Staff in Fall. Since its upload, it has received 22 views. For similar materials see /class/206999/als-5932-university-of-florida in Agriculture and Forestry at University of Florida.




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Date Created: 09/18/15
Detectability Bill Pine ALS 5932 l Detectability I If we sample tree frogs by listening fortheir calls and then catching them in a net and on the first day cold and very windy we collect 10 individuals and then we go outthe next week warm and light rain and we collect 50 has abundance increased 5X or are conditions better for catching frogs l Detectability I The probability that an individual in a particular habitat at a particular time Will actually be observed caught seen captured detected etc l Detectability I This can lead to major sampling issues because of a Not all species or individuals were accounted for u Repeated counting of some individuals a Increased kelihood of capturing some species or individuals and not others l Detectability I Count data is really commonly taken I Intuitiver it is simple I Count data is often used as a measure of what is actually present a If it is there we assume that we nd it and count it u O en times counts from sample u 39 n ts underrepresent the true number of organisms present I This results in systematic errors in samplebased 39mates l Detectability I Count statistics include number ofanimals counted in an aerial survey from a line transect survey number of animals caught in traps in a given night etc I Counts represent some unknown fraction of the target population of animals of interest I We must know what fraction of the population this count responds to understand what the number means I Detectability ECi piNi I Here the expected count the detectability p times the N in the sample uniti I 1p is the fraction of the population present thatremains undetected I Detectability ECi piNi I Here the expected count the detectability p times the N in the sample uniti I What do we usually assume aboutthis relationship I Detectability Perfect I Perfect detectability Individuals are completely detectable I I I over time space etc I Sample count is 1 identical to true Nthat P P is found in 0 I Counts provide error free comparisons across samples I Detectability Imperfect I Less than complete E C N but constant detectability p I Q is biased estimate of Ni byafactorof pi I If p can be estimated pi p lt 1 then counts can be adjusted to provide unbiased estimates of abundance I Detectability I Counts represent biased estimates of Ni piNi I The bias is nonuniform overtime addin variability and additional bias p i lt 1 i 1 Detectability issues are really common I Detectability most commonly varies over time and space in Diversity indicies rare species sampling a Linetransect sampling a Pointcounts u Markrecapture 1 Habitat use 1 Availability I Things aren t always What they seem Detectability issues are really common Detectability issues are really common Detectability issues are really common Detectability issues are really common I Detectability OK how can we estimate abundance piNi I Detectability Take the count divide it by the detectability or the probability of detection BlQ l Detectability Detectability lmaglne a perfect World 9 N rarely ever7 know 9 N here We now the the detectabtltty an detectabtl t P lnstead We rnust P We capture too mlce H v assume M We transect a nlght oftrapplng an or estlrnatel fro t We know the det tr h 100 A data u Hy rnar r 100 probabllltyls 0 25 m 400N recapture m400 Line transect Line transect I Establish a series of lines of known length in I 5W3 quotawed blOlOQlSl the area of interest assumes detect l ab my I Lines can overlap sample with replacement or not overlap sample without replac ment I Biologist walks e h u Wtdelyused ln reef swims drives ies along ac line and records what she sees specles denslty Lrne transect Stnp transect btologtst tabmty tdely used ln reef stumes tn quanttfyftsh carat and tnvenebrate abundance Reasunabletu assume detectlun at 1777 Line transect I Surrogate studies ie Jeff Laake stakes woo en doves John Witzig generally show the detection probability is not 1 and usually much lower than would be reasonably guessed 0608 terrestrial 0405 aquatic I And these are done with surrogate species that don t swim off and don t hide Line transect I Distance type methods are a major in rovement over stri transect as they attempt to incorporate detectability into the transect sample I The perpendicular distance from the line to the object is estimated or the actual distance and the angle to the individual from the line a httpAnwwruwpastandacukdistance Multiepass or depletion I Each time we walk down a path we are likely to see something new a Repeatedly sample an area and count number of species of number of a species measured in each sampling event Multiepass or depletion I Example Meador et al 2003 TAFS1323946 I Assess ef cacy of singlepass sampling in 10 river basins as part of standard monitoring program I Species richness pass 1 807100 per basin based on at least seven samples per basin I Individual sites ranged from 40100 Multiepass or depletion I Example Meador et al 2003 TAFS 13239 46 I Second sample yielded new species 503 of the time I Inverse relationship between proportion of species captured on rst event and stream size Multiepass or depletion Flrsbpass praponlnnal species richness am rsuncizln39nftmnnd39udm tn mt design inn mammu userer I Mark recapture I Mark recapture I Estimating detection probability is key in a m rkrecapture study to estimate abundance I Ratios of the markedunmarked animals are then used to estimate capture probability population trends survival diversity Intuitivey simple dif cult to do occupanc et I General approach is to create a known I Animals move about tags are lost animals leave the sampling site population of animals by tagging and releasing these animals and then try and recapture them in Fish are particularly nasty creatures to work with l Mark recapture l Mark recapture I Example I Before you can check a fish for a tag you first I Using JollySeber type mark recapture haVe t0 catCh the Sh models estimated capture probabilities for flathead catfish to be 005 for a NC rive I Capture probabilities for most fish n used radiotelemetry to evaluate my detectability sh populations are 01 or estimates for athead cat in Sample examine sh for tags in Then virtually samplequot and see how many radio tagged sh should have been caught in 67 chances to capture 4 caught 06 l Detectability I Take home message i What you see might not be all that is there i Detectability can be a major source of uncertainty in Creative approaches often needed to estimate detectability in many situations in Key parameter to think about over the next few weeks of lecture and la


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