Sources of bias in applying close-kin mark-recapture to terrestrial game species with different life histories

被引:3
|
作者
Seveque, Anthony [1 ,8 ]
Lonsinger, Robert C. [2 ]
Waits, Lisette P. [3 ]
Brzeski, Kristin E. [4 ]
Komoroske, Lisa M. [5 ]
Ott-Conn, Caitlin N. [6 ]
Mayhew, Sarah L. [7 ]
Norton, D. Cody [6 ]
Petroelje, Tyler R. [6 ]
Swenson, John D. [5 ]
Morin, Dana J. [1 ]
机构
[1] Mississippi State Univ, Forest & Wildlife Res Ctr, Dept Wildlife Fisheries & Aquaculture, Mississippi State, MS 39762 USA
[2] Oklahoma State Univ, Oklahoma Cooperat Fish & Wildlife Res Unit, US Geol Survey, Stillwater, OK USA
[3] Univ Idaho, Dept Fish & Wildlife Resources, Moscow, ID USA
[4] Michigan Technol Univ, Coll Forest Resources & Environm Sci, Houghton, MI USA
[5] Univ Massachusetts Amherst, Dept Environm Conservat, Amherst, MA USA
[6] Michigan Dept Nat Resources, Wildlife Div, Marquette, MI USA
[7] Michigan Dept Nat Resources, Wildlife Div, Lansing, MI USA
[8] Senckenberg Gesell Nat Forsch, Biodivers & Climate Res Ctr, Senckenberganlage 25, D-60325 Frankfurt, Germany
基金
美国食品与农业研究所;
关键词
abundance estimation; close-kin mark-recapture; harvest; life history traits; mating system; sampling bias; survival probability; terrestrial species; WHITE-TAILED DEER; SEXUAL SELECTION; MATING SYSTEM; BLACK BEARS; AGE; ABUNDANCE; SIZE; CONSEQUENCES; POPULATIONS; COMPETITION;
D O I
10.1002/ecy.4244
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Close-kin mark-recapture (CKMR) is a method analogous to traditional mark-recapture but without requiring recapture of individuals. Instead, multilocus genotypes (genetic marks) are used to identify related individuals in one or more sampling occasions, which enables the opportunistic use of samples from harvested wildlife. To apply the method accurately, it is important to build appropriate CKMR models that do not violate assumptions linked to the species' and population's biology and sampling methods. In this study, we evaluated the implications of fitting overly simplistic CKMR models to populations with complex reproductive success dynamics or selective sampling. We used forward-in-time, individual-based simulations to evaluate the accuracy and precision of CKMR abundance and survival estimates in species with different longevities, mating systems, and sampling strategies. Simulated populations approximated a range of life histories among game species of North America with lethal sampling to evaluate the potential of using harvested samples to estimate population size. Our simulations show that CKMR can yield nontrivial biases in both survival and abundance estimates, unless influential life history traits and selective sampling are explicitly accounted for in the modeling framework. The number of kin pairs observed in the sample, in combination with the type of kinship used in the model (parent-offspring pairs and/or half-sibling pairs), can affect the precision and/or accuracy of the estimates. CKMR is a promising method that will likely see an increasing number of applications in the field as costs of genetic analysis continue to decline. Our work highlights the importance of applying population-specific CKMR models that consider relevant demographic parameters, individual covariates, and the protocol through which individuals were sampled.
引用
收藏
页数:16
相关论文
共 27 条
  • [1] Close-Kin Mark-Recapture
    Bravington, Mark V.
    Skaug, Hans J.
    Anderson, Eric C.
    STATISTICAL SCIENCE, 2016, 31 (02) : 259 - 274
  • [2] Close-kin mark-recapture informs critically endangered terrestrial mammal status
    Luke R. Lloyd-Jones
    Mark V. Bravington
    Kyle N. Armstrong
    Emma Lawrence
    Pierre Feutry
    Christopher M. Todd
    Annabel Dorrestein
    Justin A. Welbergen
    John M. Martin
    Karrie Rose
    Jane Hall
    David N. Phalen
    Isabel Peters
    Shane M. Baylis
    Nicholas A. Macgregor
    David A. Westcott
    Scientific Reports, 13
  • [3] Close-kin mark-recapture informs critically endangered terrestrial mammal status
    Lloyd-Jones, Luke R. R.
    Bravington, Mark V. V.
    Armstrong, Kyle N. N.
    Lawrence, Emma
    Feutry, Pierre
    Todd, Christopher M. M.
    Dorrestein, Annabel
    Welbergen, Justin A. A.
    Martin, John M. M.
    Rose, Karrie
    Hall, Jane
    Phalen, David N. N.
    Peters, Isabel
    Baylis, Shane M. M.
    Macgregor, Nicholas A. A.
    Westcott, David A. A.
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [4] Considering sampling bias in close-kin mark-recapture abundance estimates of Atlantic salmon
    Wacker, Sebastian
    Skaug, Hans J.
    Forseth, Torbjorn
    Solem, Oyvind
    Ulvan, Eva M.
    Fiske, Peder
    Karlsson, Sten
    ECOLOGY AND EVOLUTION, 2021, 11 (09): : 3917 - 3932
  • [5] Expanding the feasibility of fish and wildlife assessments with close-kin mark-recapture
    Marcy-Quay, Benjamin
    Sethi, Suresh A.
    Therkildsen, Nina O.
    Kraft, Clifford E.
    ECOSPHERE, 2020, 11 (10):
  • [6] Close-kin mark-recapture methods to estimate demographic parameters of mosquitoes
    Sharma, Yogita
    Bennett, Jared B.
    Rasic, Gordana
    Marshall, John M.
    PLOS COMPUTATIONAL BIOLOGY, 2022, 18 (12)
  • [7] Estimating effective population size using close-kin mark-recapture
    Babyn, Jonathan
    Ruzzante, Daniel
    Bravington, Mark
    Flemming, Joanna Mills
    METHODS IN ECOLOGY AND EVOLUTION, 2024, 15 (11): : 2059 - 2073
  • [8] Validation of close-kin mark-recapture (CKMR) methods for estimating population abundance
    Ruzzante, Daniel E.
    McCracken, Gregory R.
    Forland, Brage
    MacMillan, John
    Notte, Daniela
    Buhariwalla, Colin
    Flemming, Joanna Mills
    Skaug, Hans
    METHODS IN ECOLOGY AND EVOLUTION, 2019, 10 (09): : 1445 - 1453
  • [9] A review of genomics methods and bioinformatics tools for the analysis of close-kin mark-recapture
    Casas, Laura
    Saborido-Rey, Fran
    FRONTIERS IN MARINE SCIENCE, 2023, 10
  • [10] Close-kin mark-recapture abundance estimation: practical insights and lessons learned
    Trenkel, Verena M.
    Charrier, Gregory
    Lorance, Pascal
    Bravington, Mark, V
    ICES JOURNAL OF MARINE SCIENCE, 2022, 79 (02) : 413 - 422