Multi-image flock size estimation with CountEm: A casestudy with half a million Common Eiders and Greater Snow Geese
Many of the methods used for estimating population size from ecological surveys have limitations on precision, cost, and/or applicability. The CountEm method was proposed recently for estimating the number of individuals in large groups from single images. It is simple and efficient, and can be appl...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unican.es:10902/27490 |
| Acceso en línea: | https://hdl.handle.net/10902/27490 |
| Access Level: | acceso abierto |
| Palabra clave: | CountEm ECA Flocks data set Flock size estimation Geometric sampling Population size estimation Quadrats |
| Sumario: | Many of the methods used for estimating population size from ecological surveys have limitations on precision, cost, and/or applicability. The CountEm method was proposed recently for estimating the number of individuals in large groups from single images. It is simple and efficient, and can be applied to any species. Here we present a case study by applying CountEm to a real ecological survey with 278 images of Greater Snow Geese (Anser caerulescens atlanticus) and Common Eiders (Somateria mollissima) flocks taken from fixed-wing aircraft in Eastern Canada. First, we evaluated the precision and counting time of CountEm on single images. Second, we developed and tested a new multi-image version of the CountEm software. We show that flock sizes of N?>?35,000 can be estimated on single images in ?5 min, from counting a sample of ?200 birds, yielding relative SEs in the 5%?10% range. Processing times increased to 10?20?min when simultaneously processing large numbers of images that contained over half a million birds with only modest increases in relative SE (range: 10%?15%). Our results suggest that CountEm may be used to save time and resources if incorporated into monitoring programs that utilize imagery in the abundance estimates. |
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