A novel sampling approach to estimating abundance of low‐density and observable species

Abstract Informative species abundance estimates are critical for guiding decisions around the conservation and management of ecological systems. There exist many methods for estimating abundance of frequently encountered species and populations with uniquely identifiable individuals. However, for w...

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Autores principales:	Molly C. McDevitt, E. Frances Cassirer, Shane B. Roberts, Paul M. Lukacs
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	assumptions Idaho monitoring mountain goats multiple observer Oreamnos americanus Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/746d297b6342469b9d7ae72d75c74bff
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spelling	oai:doaj.org-article:746d297b6342469b9d7ae72d75c74bff2021-11-29T07:06:42ZA novel sampling approach to estimating abundance of low‐density and observable species2150-892510.1002/ecs2.3815https://doaj.org/article/746d297b6342469b9d7ae72d75c74bff2021-11-01T00:00:00Zhttps://doi.org/10.1002/ecs2.3815https://doaj.org/toc/2150-8925Abstract Informative species abundance estimates are critical for guiding decisions around the conservation and management of ecological systems. There exist many methods for estimating abundance of frequently encountered species and populations with uniquely identifiable individuals. However, for wildlife populations with unmarked individuals that occur at low densities, there exist a variety of behaviors and characteristics that make effectively surveying and sampling challenging or uninformative. Examples of challenging characteristics include the elusive behaviors of low‐density species that occur in complex and rugged terrain. Such characteristics make detection difficult and surveys expensive, dangerous, and potentially biased. To address these challenges, we used a common, non‐invasive field survey method combined with a probability‐based study design and frequently utilized statistical model to estimate abundance of an unmarked mountain goat population in eastern Idaho. We developed a novel data analysis approach using an N‐mixture model that, together with spatially balanced random sampling and a double‐observer field data collection method, directly solves the problem of approximating statistical assumptions, including population closure. We demonstrate that a probability‐based sampling design not only is feasible, but also is important for estimating population parameters for unmarked and low‐density species. With this approach, we present a procedure that offers unbiased abundance estimates, empowering managers to track low‐density species’ population trends across time.Molly C. McDevittE. Frances CassirerShane B. RobertsPaul M. LukacsWileyarticleassumptionsIdahomonitoringmountain goatsmultiple observerOreamnos americanusEcologyQH540-549.5ENEcosphere, Vol 12, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	assumptions Idaho monitoring mountain goats multiple observer Oreamnos americanus Ecology QH540-549.5
spellingShingle	assumptions Idaho monitoring mountain goats multiple observer Oreamnos americanus Ecology QH540-549.5 Molly C. McDevitt E. Frances Cassirer Shane B. Roberts Paul M. Lukacs A novel sampling approach to estimating abundance of low‐density and observable species
description	Abstract Informative species abundance estimates are critical for guiding decisions around the conservation and management of ecological systems. There exist many methods for estimating abundance of frequently encountered species and populations with uniquely identifiable individuals. However, for wildlife populations with unmarked individuals that occur at low densities, there exist a variety of behaviors and characteristics that make effectively surveying and sampling challenging or uninformative. Examples of challenging characteristics include the elusive behaviors of low‐density species that occur in complex and rugged terrain. Such characteristics make detection difficult and surveys expensive, dangerous, and potentially biased. To address these challenges, we used a common, non‐invasive field survey method combined with a probability‐based study design and frequently utilized statistical model to estimate abundance of an unmarked mountain goat population in eastern Idaho. We developed a novel data analysis approach using an N‐mixture model that, together with spatially balanced random sampling and a double‐observer field data collection method, directly solves the problem of approximating statistical assumptions, including population closure. We demonstrate that a probability‐based sampling design not only is feasible, but also is important for estimating population parameters for unmarked and low‐density species. With this approach, we present a procedure that offers unbiased abundance estimates, empowering managers to track low‐density species’ population trends across time.
format	article
author	Molly C. McDevitt E. Frances Cassirer Shane B. Roberts Paul M. Lukacs
author_facet	Molly C. McDevitt E. Frances Cassirer Shane B. Roberts Paul M. Lukacs
author_sort	Molly C. McDevitt
title	A novel sampling approach to estimating abundance of low‐density and observable species
title_short	A novel sampling approach to estimating abundance of low‐density and observable species
title_full	A novel sampling approach to estimating abundance of low‐density and observable species
title_fullStr	A novel sampling approach to estimating abundance of low‐density and observable species
title_full_unstemmed	A novel sampling approach to estimating abundance of low‐density and observable species
title_sort	novel sampling approach to estimating abundance of low‐density and observable species
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/746d297b6342469b9d7ae72d75c74bff
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A novel sampling approach to estimating abundance of low‐density and observable species

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