Using DNA to test the utility of pellet-group counts as an index of deer counts
Despite widespread use of fecal pellet-group counts as an index of ungulate density, techniques used to convert pellet-group numbers to ungulate numbers rarely are based on counts of known individuals, seldom evaluated across spatial and temporal scales, and precision is infrequently quantified. Usi...
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| Veröffentlicht in: | Wildlife Society bulletin Jg. 37; H. 2; S. 444 - 450 |
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Blackwell Publishing Ltd
01.06.2013
Wildlife Society Wiley |
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| ISSN: | 1938-5463, 2328-5540, 1938-5463, 2328-5540 |
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| Abstract | Despite widespread use of fecal pellet-group counts as an index of ungulate density, techniques used to convert pellet-group numbers to ungulate numbers rarely are based on counts of known individuals, seldom evaluated across spatial and temporal scales, and precision is infrequently quantified. Using DNA from fecal pellets to identify individual deer, we evaluated the relationship between pellet-group count and count of Sitka black-tailed deer (Odocoileus hemionus sitkensis) during a 3-year study (2006–2008) in 3 watersheds in southeast Alaska, USA. We surveyed 141,054 m2of transect, counted 10,569 pellet groups, and identified 737 unique deer. We used a multilevel mixed-effects generalized linear model to analyze expected deer count as a function of pellet-group count. Pellet-group count was a significant predictor of DNA-based index of deer count, but that relationship varied by transect, watershed, and year, indicating that extrapolation of a single linear relationship across space and time was not possible. More importantly, most of the variation in ourmodels was residual and unexplained. Assuming that our DNA-based results were amore accurate and precise metric of true deer count, we do not support the use of pellet-group count to index deer count in southeast Alaska unless confounding factors are accounted for at fine spatial (e.g., habitat patch) scales. Because of the difficulty in routinely evaluating the influence of confounding variables in remote and unmanaged landscapes, we suggest that wildlife programs in these environments consider alternatives, such as DNA-based methods, for monitoring trends in ungulate populations. |
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| AbstractList | Despite widespread use of fecal pellet-group counts as an index of ungulate density, techniques used to convert pellet-group numbers to ungulate numbers rarely are based on counts of known individuals, seldom evaluated across spatial and temporal scales, and precision is infrequently quantified. Using DNA from fecal pellets to identify individual deer, we evaluated the relationship between pellet-group count and count of Sitka black-tailed deer (Odocoileus hemionus sitkensis) during a 3-year study (2006–2008) in 3 watersheds in southeast Alaska, USA. We surveyed 141,054 m2of transect, counted 10,569 pellet groups, and identified 737 unique deer. We used a multilevel mixed-effects generalized linear model to analyze expected deer count as a function of pellet-group count. Pellet-group count was a significant predictor of DNA-based index of deer count, but that relationship varied by transect, watershed, and year, indicating that extrapolation of a single linear relationship across space and time was not possible. More importantly, most of the variation in ourmodels was residual and unexplained. Assuming that our DNA-based results were amore accurate and precise metric of true deer count, we do not support the use of pellet-group count to index deer count in southeast Alaska unless confounding factors are accounted for at fine spatial (e.g., habitat patch) scales. Because of the difficulty in routinely evaluating the influence of confounding variables in remote and unmanaged landscapes, we suggest that wildlife programs in these environments consider alternatives, such as DNA-based methods, for monitoring trends in ungulate populations. Abstract Despite widespread use of fecal pellet‐group counts as an index of ungulate density, techniques used to convert pellet‐group numbers to ungulate numbers rarely are based on counts of known individuals, seldom evaluated across spatial and temporal scales, and precision is infrequently quantified. Using DNA from fecal pellets to identify individual deer, we evaluated the relationship between pellet‐group count and count of Sitka black‐tailed deer (Odocoileus hemionus sitkensis) during a 3‐year study (2006–2008) in 3 watersheds in southeast Alaska, USA. We surveyed 141,054 m2 of transect, counted 10,569 pellet groups, and identified 737 unique deer. We used a multilevel mixed‐effects generalized linear model to analyze expected deer count as a function of pellet‐group count. Pellet‐group count was a significant predictor of DNA‐based index of deer count, but that relationship varied by transect, watershed, and year, indicating that extrapolation of a single linear relationship across space and time was not possible. More importantly, most of the variation in our models was residual and unexplained. Assuming that our DNA‐based results were a more accurate and precise metric of true deer count, we do not support the use of pellet‐group count to index deer count in southeast Alaska unless confounding factors are accounted for at fine spatial (e.g., habitat patch) scales. Because of the difficulty in routinely evaluating the influence of confounding variables in remote and unmanaged landscapes, we suggest that wildlife programs in these environments consider alternatives, such as DNA‐based methods, for monitoring trends in ungulate populations. © 2013 The Wildlife Society. Despite widespread use of fecal pellet‐group counts as an index of ungulate density, techniques used to convert pellet‐group numbers to ungulate numbers rarely are based on counts of known individuals, seldom evaluated across spatial and temporal scales, and precision is infrequently quantified. Using DNA from fecal pellets to identify individual deer, we evaluated the relationship between pellet‐group count and count of Sitka black‐tailed deer (Odocoileus hemionus sitkensis) during a 3‐year study (2006–2008) in 3 watersheds in southeast Alaska, USA. We surveyed 141,054 m2 of transect, counted 10,569 pellet groups, and identified 737 unique deer. We used a multilevel mixed‐effects generalized linear model to analyze expected deer count as a function of pellet‐group count. Pellet‐group count was a significant predictor of DNA‐based index of deer count, but that relationship varied by transect, watershed, and year, indicating that extrapolation of a single linear relationship across space and time was not possible. More importantly, most of the variation in our models was residual and unexplained. Assuming that our DNA‐based results were a more accurate and precise metric of true deer count, we do not support the use of pellet‐group count to index deer count in southeast Alaska unless confounding factors are accounted for at fine spatial (e.g., habitat patch) scales. Because of the difficulty in routinely evaluating the influence of confounding variables in remote and unmanaged landscapes, we suggest that wildlife programs in these environments consider alternatives, such as DNA‐based methods, for monitoring trends in ungulate populations. © 2013 The Wildlife Society. |
| Author | Leonawicz, Matthew Brinkman, Todd J. Person, David K. Smith, Winston McCoy, Karin Hundertmark, Kris J. Chapin III, F. Stuart |
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| Cites_doi | 10.1046/j.0305-1838.2003.00026.x 10.2193/2005-695 10.32800/abc.2004.27.0217 10.1111/j.1469-7998.1985.tb04910.x 10.1007/s10651-007-0030-3 10.1111/j.1541-0420.2008.01165.x 10.2307/3809641 10.1016/j.ecolind.2010.04.006 10.1093/genetics/160.1.357 10.1111/j.1365-2028.1988.tb00962.x 10.2307/3798941 10.2193/0022-541X(2005)069<0322:WHSBSB>2.0.CO;2 10.2193/2008-007 10.1093/forestscience/26.3.448 10.1002/jwmg.22 10.1111/j.1365-2664.2008.01512.x 10.1644/1545-1542(2000)081<1035:AETOUC>2.0.CO;2 10.1111/j.1469-1795.2009.00238.x 10.1016/0006-3207(93)90135-N 10.1111/j.1365-2907.1984.tb00341.x 10.2307/1940131 10.2193/0022-541X(2006)70[1403:RFAMOB]2.0.CO;2 10.2307/3808966 10.2307/3796010 10.2307/3801624 10.1007/s12686-010-9176-7 10.1111/j.0021-8901.2004.00964.x 10.2193/0022-541X(2005)069<0396:EPSFDC>2.0.CO;2 10.2193/0022-541X(2005)69[1419:NGSTFW]2.0.CO;2 |
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| References_xml | – reference: Slade, N. A., and S. M. Blair. 2000. An empirical test of using counts of individuals captured as indices of population size. Journal of Mammalogy 81:1035-1045. – reference: Campbell, D., G. M. Swanson, and J. Sales. 2004. Comparing the precision and cost-effectiveness of faecal pellet group count methods. Journal of Applied Ecology 41:1185-1196. – reference: Staines, B. W., and P. R. Ratcliffe. 1987. Estimating the abundance of red deer (Cervus elaphus L.) and roe deer (Capreolus capreolus L.) and their current status in Great Britain. Symposia of the Zoological Society of London 58:131-152. – reference: Alaback, P. B. 1982. Dynamics of understory biomass in Sitka spruce-western hemlock forests of southeast Alaska. Ecology 63:1932-1948. – reference: Lukacs, P. M., and K. P. Burnham. 2005. Estimating population size from DNA-based closed capture-recapture data incorporating genotyping error. 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| Snippet | Despite widespread use of fecal pellet-group counts as an index of ungulate density, techniques used to convert pellet-group numbers to ungulate numbers rarely... Despite widespread use of fecal pellet‐group counts as an index of ungulate density, techniques used to convert pellet‐group numbers to ungulate numbers rarely... Abstract Despite widespread use of fecal pellet‐group counts as an index of ungulate density, techniques used to convert pellet‐group numbers to ungulate... |
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| SubjectTerms | Alaska Censuses Deer DNA fecal pellets Forest habitats Habitat conservation Habitat selection Odocoileus hemionus sitkensis pellet-group counts Sitka black-tailed deer Statistical variance Tools and Technology Ungulates Watersheds Wildlife management Wildlife population estimation |
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| Title | Using DNA to test the utility of pellet-group counts as an index of deer counts |
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