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Czasopismo

2020 | 164 | 07 |

Tytuł artykułu

Wpływ cech populacji i środowiska na dokładność i precyzję wyników symulacji lotniczej inwentaryzacji zwierzyny

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Treść / Zawartość

Warianty tytułu

EN
Effects of population and habitat characteristics on the accuracy and precision of wildlife aerial surveys results

Języki publikacji

PL

Abstrakty

EN
Estimation of population abundance is one of the most difficult tasks in wildlife management. In case of forest−dwelling ungulates, none of the currently available survey methods is satisfying in terms of accuracy, precision, and cost−effectiveness. Therefore, we propose a new method of ungulate monitoring based on distance sampling and using unmanned aerial vehicles equipped with thermal infrared cameras. The method is potentially more reliable and cost−effective than conventional survey techniques. It also allows for aerial surveys in the dark when animals are most active. However, the method needs to be tested before wide−scale implementation in wildlife management practice. While the effects of sampling design and effort on accuracy and precision of abundance estimates are well recognized, the importance of population and habitat characteristics is often overlooked by wildlife managers. We used simulations to assess the effects of population size, animal aggregation, and habitat−depended detection probability on the accuracy and precision of wildlife aerial survey results. We created 1000 virtual populations defined by population density (2−22 individuals/100 ha), mean group size (1−6 individuals), and probability of animal detection during surveys (proportional to canopy cover, 30−60%). Animals were distributed on a virtual study area (5000 ha) according to randomly generated density distribution. Each population was subjected to 25 simulated surveys using the same design (39 transects grouped in three 2.0×2.5 km blocks). The transects covered 12% of the entire study area. We used conventional distance sampling to estimate abundance and generalized linear models to assess the effect of each parameter on the accuracy and precision of estimates. The estimation accuracy was mostly affected by the probability of detection (β=–0.75) and, to a lesser degree, by aggregation (β=–0.25) and population size (β=0.09). Precision was influenced by the aggregation (β=0.32) and population size (β=–0.26), while detection probability had a weaker effect (β=–0.11). Observed significant differences in quality of abundance estimates derived by the same survey design, but with differing population and habitat characteristics, indicate that each survey requires an individual approach. It is impossible to formulate general recommendations, e.g. concerning flight plan or area coverage. To achieve the required level of precision, while minimizing the survey costs, it is necessary to test alternative survey designs with the aid of computer simulations.

Słowa kluczowe

Wydawca

-

Czasopismo

Rocznik

Tom

164

Numer

07

Opis fizyczny

s.560-567,rys.,bibliogr.

Twórcy

autor
  • Muzeum i Instytut Zoologii Polskiej Akademii Nauk, ul. Wilcza 64, 00-679 Warszawa
autor
  • Muzeum i Instytut Zoologii Polskiej Akademii Nauk, ul. Wilcza 64, 00-679 Warszawa

Bibliografia

  • Buckland S. T., Anderson D. R., Burnham K. P., Laake J. L., Borchers D. L., Thomas L. 2001. Introduction to distance sampling: Estimating abundance of biological populations. Oxford University Press, Oxford.
  • Marshall L. 2019. DSsim: Distance sampling simulations. R package version 1.1.4.
  • Nuno A., Bunnefeld N., Milner-Gulland E. J. 2013. Matching observations and reality: using simulation models to improve monitoring under uncertainty in the Serengeti. Journal of Applied Ecology 50 (2): 488-498. DOI: https://doi.org/10.1111/1365-2664.12051.
  • Pagacz S., Witczuk J. 2016. Wykorzystanie samolotów bezzałogowych i termowizji do nocnej inwentaryzacji kopytnych. Studia i Materiały CEPL 49A: 50-57.
  • Pierce B. L., Lopez R. R., Silvy N. J. 2012. Estimating animal abundance. W: Silvy N. J. [red.]. The wildlife techniques manual: Research. The Johns Hopkins University Press, Baltimore, Maryland. 284-310.
  • Witczuk J., Pagacz S., Mills L. S. 2008. Optimising methods for monitoring programs: Olympic marmots as a case study. Wildlife Research 35 (8): 788-797. DOI: https://doi.org/10.1071/WR07187.
  • Witczuk J., Pagacz S., Zmarz A., Cypel M. 2018. Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests – preliminary results. International Journal of Remote Sensing 39 (15-16): 5504-5521. DOI: https://doi.org/10.1080/01431161.2017.1390621.
  • Zurell D., Berger U., Cabral J. S., Jeltsch F., Meynard C. N., Münkemüller T., Nehrbass N., Pagel J., Reineking B., Schröder B., Grimm V. 2010. The virtual ecologist approach: simulating data and observers. Oikos 119 (4): 622-635. DOI: https://doi.org/10.1111/j.1600-0706.2009.18284.x.

Typ dokumentu

Bibliografia

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