Assessing the impacts of imperfect detection on estimates of diversity and community structure through multispecies occupancy modeling.

David Benoit,Donald A Jackson,Mark S Ridgway

doi:10.1002/ece3.4023

David Benoit, Donald A Jackson + Show 1 more

Open Access

https://doi.org/10.1002/ece3.4023

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Abstract

Detecting all species in a given survey is challenging, regardless of sampling effort. This issue, more commonly known as imperfect detection, can have negative impacts on data quality and interpretation, most notably leading to false absences for rare or difficult‐to‐detect species. It is important that this issue be addressed, as estimates of species richness are critical to many areas of ecological research and management. In this study, we set out to determine the impacts of imperfect detection, and decisions about thresholds for inclusion in occupancy, on estimates of species richness and community structure. We collected data from a stream fish assemblage in Algonquin Provincial Park to be used as a representation of ecological communities. We then used multispecies occupancy modeling to estimate species‐specific occurrence probabilities while accounting for imperfect detection, thus creating a more informed dataset. This dataset was then compared to the original to see where differences occurred. In our analyses, we demonstrated that imperfect detection can lead to large changes in estimates of species richness at the site level and summarized differences in the community structure and sampling locations, represented through correspondence analyses.

Highlights

Detecting all species present in a given survey is challenging, regardless of sampling effort (Iknayan, Tingley, Furnas, & Beissinger, 2014; Royle, Nichols, & Kery, 2005), yet it is critical for many areas of research in ecology and in management issues
An alternative approach that has been put forth to tackle these issues and fully address imperfect detection is that of multispecies occupancy modeling
Studies examining ecological communities will likely have various degrees of undetected species at one or more locations within the survey data; the degree to which such data are missing is rarely considered, let alone formally evaluated. We examine this issue of sampling underestimation through multispecies occupancy modeling

Summary

| INTRODUCTION

Detecting all species present in a given survey is challenging, regardless of sampling effort (Iknayan, Tingley, Furnas, & Beissinger, 2014; Royle, Nichols, & Kery, 2005), yet it is critical for many areas of research in ecology and in management issues (e.g. conservation biology, invasive species). An alternative approach that has been put forth to tackle these issues and fully address imperfect detection is that of multispecies occupancy modeling This form of modeling, developed by Dorazio, Royle, Soderstrom, and Glimskar (2006), can be used to estimate species-specific occurrence probabilities, while accounting for variability in detection from numerous sources (MacKenzie et al, 2006; Zipkin, DeWan, & Royle, 2009). Studies examining ecological communities will likely have various degrees of undetected species at one or more locations within the survey data; the degree to which such data are missing is rarely considered, let alone formally evaluated We examine this issue of sampling underestimation through multispecies occupancy modeling. We propose an approach to evaluate the impact of sampling underestimation on standard analytical methods, such as ordination analysis of ecological communities, given that these multivariate methods are employed widely in ecological studies

| METHODS

| DISCUSSION

Findings

CONFLICT OF INTEREST