Application of distance sampling techniques for diving ducks on Lake St. Clair and western Lake Erie

Shirkey, Brendan T.; Luukkonen, David R.; Winterstein, Scott R.

doi:10.1016/j.jglr.2014.02.015

Cited by 3 publications

(8 citation statements)

References 11 publications

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“…However, these transect‐based approaches have been primarily conducted on relatively large and uniform regions (e.g., Lower Mississippi Alluvial Valley; Reinecke et al , Pearse et al ). Attempts to employ probability‐based sampling techniques during the nonbreeding season for use at smaller scales or in riverine areas, which often contain discontinuous and irregular habitat, have rarely been implemented (but see Fleskes and Yee , Shirkey et al ). Grid‐based designs incorporating helicopters have been shown to work well in such habitats during the breeding season (Cordts et al , Zicus et al , Giudice et al ); yet, aside from Eggeman et al (), a grid‐based approach has not been conducted during the nonbreeding season with a fixed‐wing aircraft.…”

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confidence: 99%

An aerial grid‐based sampling method to estimate nonbreeding duck abundance

et al. 2017

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Probability-based sampling designs for aerial surveys are useful for estimating wintering waterfowl abundances in large areas with contiguous habitat (e.g., Mississippi Alluvial Valley). The effectiveness of these approaches for estimating abundance of nonbreeding waterfowl in small areas with discontinuous habitat has rarely been assessed. Surveys conducted within riverine areas introduce sampling design difficulties because of discontinuous bottomland habitat and irregularity of flooding events. Therefore, we implemented and evaluated a generalizable grid-based, stratified random sampling design to estimate weekly duck abundance within the Wabash River floodplain in southeastern Illinois and southwestern Indiana, USA, during midwinter and early spring 2012 and 2013. We used aerial and ground-based counts to generate abundance estimates and evaluate within-week variation of counts. Peak duck abundance totaled 279,717 (SE ¼ 69,101) in 2012 and 742,027 (SE ¼ 296,563) in 2013. Dabbling ducks were the most abundant duck guild detected and abundance estimates for this group met our precision goal (CV 0.25) in 4 of 18 surveys ( x ¼ 0.33, range ¼ 0.20-0.51). We compared the grid-based approach with a traditional aerial inventory and found that estimates from the grid-based approach were on average 75,175 (SE ¼ 46,768) greater than inventory counts. Desired levels of precision were often not met, yet design improvements in 2013 notably increased precision, suggesting that further modifications may allow this method to be useful in riverine areas with discontinuous and ephemeral habitat. Ó 2017 The Wildlife Society.

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confidence: 99%

An aerial grid‐based sampling method to estimate nonbreeding duck abundance

et al. 2017

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“…For example, common definitions of hotspots focus on determining areas with consistent high species abundance (Davoren, ; Piatt et al, ), richness or biological activity (Sydeman, Brodeur, Grimes, Bychkov, & McKinnell, ) or some combination of these (Nur et al, ). Hotspots have also been defined as locations where some metric exceeds a predefined threshold, such as the top five percent of the data (Harvey et al, ) or locations outside one (Santora & Veit, ; Suryan, Santora, & Veit, ) or three (Zipkin et al, ) standard deviations above the mean of a particular region or area sampled. Such definitions attempt to quantify hotspots (allowing for direct location comparison) as opposed to identifying hotspots using only qualitative criteria, which was common until recently (Mittermeier, Turner, Larsen, Brooks, & Gascon, ).…”

Section: Introductionmentioning

confidence: 99%

“…As such, the foremost method to determine patterns of waterbird species is to identify locations of persistent aggregation or high use, such as hotspots. Hotspot identification is useful in studies of highly mobile organisms, such as waterbirds, because the likelihood that a survey event of any given location is representative of true abundance at that location is low due to the extreme variability of their distributions (Santora & Veit, ). There are many methods to examine the diversity and abundance patterns of open water populations, but locating persistent high‐use areas is a frequent first step towards understanding the processes that generate spatial patterns of species distributions and informing effective conservation action (Nelson & Boots, ).…”

Section: Introductionmentioning

confidence: 99%

“…As their name suggests, these techniques account for spatial patterns in the data (Harvey et al, ). In contrast, classic statistical modelling techniques, which use GLM‐based frameworks, do not typically consider explicit spatial correlation in hotspot identification and instead require the use of statistical distributions and a metric or threshold to account for variations in abundance patterns (Oppel et al, ; Santora & Veit, ; Zipkin et al, ). Waterbirds are highly mobile and tend to aggregate in large groups, resulting in highly skewed data with many absences in space and over time.…”

Section: Introductionmentioning

confidence: 99%

“…For the other two models, we adapted GLM‐based techniques which have been used to identify waterbird hotspot locations. Hotspot persistence defines hotspots for every unique sampling event and calculates persistence over time (Johnson, Zipkin, O’Connell, & Caldow, ; Santora & Veit, ; Suryan et al, ). Hotspots conditional on presence combines survey data from all sampling events and defines hotspots as locations with a long‐term average abundance greater than three times the regional mean, conditional on species presence (Kinlan et al, ; Zipkin et al, ).…”

Section: Introductionmentioning

confidence: 99%

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A comparative analysis of common methods to identify waterbird hotspots

et al. 2019

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Hotspot analysis is a commonly used method in ecology and conservation to identify areas of high biodiversity or conservation concern. However, delineating and mapping hotspots is subjective and various approaches can lead to different conclusions with regard to the classification of particular areas as hotspots, complicating long‐term conservation planning. We present a comparative analysis of recent approaches for identifying waterbird hotspots, with the goal of developing insights about the appropriate use of these methods. We selected four commonly used measures to identify persistent areas of high use: kernel density estimation, Getis‐Ord Gi*, hotspot persistence and hotspots conditional on presence, which represent the range of quantitative hotspot estimation approaches used in waterbird analyses. We applied each of the methods to aerial survey waterbird count data collected in the Great Lakes from 2012–2014. For each approach, we identified areas of high use for seven species/species groups and then compared the results across all methods and to mean effort‐corrected counts. Our results indicate that formal hotspot analysis frameworks do not always lead to the same conclusions. The kernel density and Getis‐Ord Gi* methods yielded the most similar results across all species analysed and were generally correlated with mean effort‐corrected count data. We found that these two models can differ substantially from the hotspot persistence and hotspots conditional on presence estimation approaches, which were not consistently similar to one another. The hotspot persistence approach differed most significantly from the other methods but is the only method to explicitly account for temporal variation. We recommend considering the ecological question and scale of conservation or management activities prior to designing survey methodologies. Deciding the appropriate definition and scale for analysis is critical for interpretation of hotspot analysis results as is inclusion of important covariates. Combining hotspot analysis methods using an integrative approach, either within a single analysis or post hoc, could lead to greater consistency in the identification of waterbird hotspots.

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Evaluation of the distance sampling technique to monitor gregarious species: A simulation exercise

Jacquemin

Vallecillo

Guillemain

et al. 2022

Wildlife Society Bulletin

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Wildlife count data are used to provide estimates of population abundance. To quantify changes in the size of populations, it is necessary to determine the precision of abundance estimates. Ground or aerial survey methods are often intended to be a census and thus do not provide estimates of precision. One solution is to switch approaches to a different sampling and estimation procedure, such as distance sampling. Using an example of the long‐term waterfowl censuses in the Camargue, a major wintering area in southern France, we used simulation to quantify the precision and cost effectiveness of transitioning to a distance sampling approach. Based on abundance and distribution data for 14 species of waterbirds (Anatidae and coot [Fulica atra]) in the Camargue, we evaluated 5 distance sampling scenarios with parallel line transects corresponding to 5 different sampling intensities. Our results suggested that, if the objective of counts is to estimate the abundance of all waterfowl together, switching from pseudo‐total to distance sampling counts would be realistic on both statistical and financial grounds. The most advantageous scenario would correspond to a sampling effort with transect lines spaced 1,000 m apart, which would allow a coefficient of variation of 0.20 for total waterfowl abundance with a 27% increase in flight costs compared to the current method. Heterogeneity in the size of detected bird groups contributed to the variability in our results, however, adjusting sampling effort from 1,750 m spaced transect to 760 m allowed reduced variation by 55%. We recommend proceeding to test flights which would allow some site‐specific parameters to be refined before changing methods. However, the distance sampling method as simulated here would prove impractical to evaluate the abundance and distribution of one species alone.

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Application of distance sampling techniques for diving ducks on Lake St. Clair and western Lake Erie

Cited by 3 publications

References 11 publications

An aerial grid‐based sampling method to estimate nonbreeding duck abundance

An aerial grid‐based sampling method to estimate nonbreeding duck abundance

A comparative analysis of common methods to identify waterbird hotspots

Evaluation of the distance sampling technique to monitor gregarious species: A simulation exercise

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