Validation of a novel time-to-event nest density estimator on passerines: An example using Brewer’s sparrows (Spizella breweri)
Autoři:
Kaitlyn M. Reintsma aff001; Alan H. Harrington aff001; Victoria J. Dreitz aff001
Působiště autorů:
Avian Science Center, Wildlife Biology Program, University of Montana, Missoula, Montana, United States of America
aff001; Animal and Rangeland Sciences, Oregon State University, Corvallis, Oregon, United States of America
aff002
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0227092
Souhrn
Human impacts on natural resources increasingly necessitate understanding of the demographic rates driving wildlife population trends. Breeding productivity in many avian species is the demographic parameter that primarily influences population fluctuations. Nest density is a vital component of breeding productivity despite the fact that it is most often inferred exclusively from nest success. Unfortunately, locating every nest in a given area to determine nest density is often not feasible and can be biased by measurement error. The availability of a nest to be detected and the probability it will be detected during nest searching are two prominent sources of measurement error. A time-to-event nest density estimator has been developed that, unlike standard distance sampling methods, accounts for availability and can use nest data from outside structured surveys routinely collected to assess nest success. Its application is currently limited to Anseriformes, so we evaluated the general applicability of the time-to-event estimator in the order Passeriformes. To do this, we compared estimates of nest detection rate and nest density from the time-to-event estimator to distance sampling methods for 42 Brewer’s sparrow (Spizella breweri) nests monitored in 2015. The time-to-event estimator produced similar but more precise nest detection and density estimates than distance sampling methods.
Klíčová slova:
Birds – Natural resources – Nesting habits – Population density – Probability density – Surveys – Wildlife – Passerines
Zdroje
1. Barnosky AD, Matzke N, Tomiya S, Wogan GO, Swartz B, Quental TB, et al. Has the Earth’s sixth mass extinction already arrived? Nature. 2011; 471(7336): 51. doi: 10.1038/nature09678 21368823
2. Lindenmayer DB, Likens GE. The science and application of ecological monitoring. Biological conservation. 2010; 143(6): 1317–1328.
3. Caswell H. Matrix population models: construction, analysis, and interpretation. 2nd ed. Sunderland, Massachusetts: Sinauer Associates; 2001.
4. Stearns SC. Life history strategies. Oxford, United Kingdom: Oxford University Press; 1992.
5. Clark ME, Martin TE. Modeling tradeoffs in avian life history traits and consequences for population growth. Ecological Modelling. 2007; 209: 110–120.
6. Pearce-Higgins JW, Green RE, Green R. Birds and climate change: impacts and conservation responses. Cambridge, United Kingdom: Cambridge University Press; 2014.
7. Gregory RD, Strien AV. Wild bird indicators: Using composite population trends of birds as measures of environmental health. Ornithological Science. 2010; 9: 3–22.
8. Thompson BC, Knadle GE, Brubaker DL, Brubaker KS. Nest success is not an adequate comparative estimate of avian reproduction. Journal of Field Ornithology. 2001; 72: 527–36.
9. Ricklefs RE, and Bloom G. Components of avian breeding productivity. The Auk. 1977; 94: 86–96.
10. Van Horne B. Abundance as a misleading indicator of habitat quality. The Journal of Wildlife Management. 1983; 47: 893.
11. Chalfoun AD, Schmidt KA. Adaptive breeding-habitat selection: is it for the birds? The Auk. 2012; 129: 589–99.
12. Johnson RG and Temple SA. Assessing habitat quality for birds nesting in fragmented tallgrass prairies. In: Verner J, Morrison ML, Ralph CJ, editors. Wildlife 2000: modeling habitat relationships of terrestrial vertebrates. Madison, Wisconsin: University of Wisconsin Press; 1986. p. 245–249.
13. Vickery PD, Hunter ML Jr, Wells JV. Is density an indicator of breeding success? The Auk. 1992; 109: 706–710.
14. Buckland ST, Anderson DR, Burnham KP, Laake JL. Distance sampling: methods and applications. Switzerland: Springer International Publishing; 2015.
15. MacKenzie DI, Nichols JD, Sutton N. Improving inferences in population studies of rare species that are detected imperfectly. Ecology. 2005; 86: 1101–1113.
16. Kellner KF, Swihart RK. Accounting for imperfect detection in ecology: a quantitative review. PLOS ONE. 2014 Oct 30. doi: 10.1371/journal.pone.0111436 25356904
17. Giovanni MD, Van Der Burg MP, Anderson LC, Powell LA, Schacht WH, Tyre AJ. Estimating nest density when detectability is incomplete: variation in nest attendance and response to disturbance by western meadowlarks. The Condor. 2011; 113(1): 223–232.
18. Magrath RD. Hatching asynchrony in altricial birds. Biological Reviews. 1990; 65: 587–622.
19. Bibby CJ, Burgess ND, Hill DA. Bird census techniques. 2nd ed. London, United Kingdom: Academic Press; 2000.
20. Baldwin F, Alisauskas RT, Leafloor JO. Nest survival and density of Cackling Geese (Branta hutchinsii) inside and outside a Ross's Goose (Chen rossii) colony. Auk. 2011; 128: 404–414.
21. Barbraud C, Vasseur J, Delord K. Using distance sampling and occupancy rate to estimate abundance of breeding pairs of Wilson’s Storm Petrel (Oceanites oceanicus) in Antarctica. Polar Biology. 2018; 41: 313–322.
22. Bächler E, Liechti F. On the importance of g(0) for estimating bird population densities with standard distance‐sampling: implications from a telemetry study and a literature review. Ibis. 2007; 149: 693–700.
23. Buckland ST. Perpendicular distance models for line transect sampling. Biometrics. 1985; 41: 177–195. 4005374
24. Péron G, Walker J, Rotella J, Hines JE, Nichols JD. Estimating nest abundance while accounting for time-to-event processes and imperfect detection. Ecology. 2014; 95: 2548–2557.
25. Graubard BI, Korn EL. Inference for superpopulation parameters using sample surveys. Statistical Science. 2002; 17: 73–96.
26. Monroe AP, Chandler RB, Burger WB Jr, Martin JA. Converting exotic forages to native warm-season grass can increase avian productivity in beef production systems. Agriculture, Ecosystems and Environment. 2016; 233: 85–93.
27. Ruehmann MB, Desmond MJ, Gould WR. Effects of smooth brome on Brewer's Sparrow nest survival in sagebrush steppe. Condor. 2011; 113: 419–428.
28. Rotenberry JT, Wiens JA. Weather and reproductive variation in shrubsteppe sparrows: a hierarchical analysis. Ecology. 1991; 72: 1325–1335.
29. Mayfield HF. Nesting success calculated from exposure. Wilson Bulletin. 1961; 73: 255–261.
30. Miller D. Distance: distance sampling detection function and abundance estimation. 2015. R package version 0.9.4.
Článek vyšel v časopise
PLOS One
2019 Číslo 12
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Methylsulfonylmethane increases osteogenesis and regulates the mineralization of the matrix by transglutaminase 2 in SHED cells
- Oregano powder reduces Streptococcus and increases SCFA concentration in a mixed bacterial culture assay
- The characteristic of patulous eustachian tube patients diagnosed by the JOS diagnostic criteria
- Parametric CAD modeling for open source scientific hardware: Comparing OpenSCAD and FreeCAD Python scripts
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy