Estimating Animal Abundance in Heterogeneous Environments: An Application to Aerial Surveys for Dugongs

Pollock, Kenneth H.; Marsh, Helene; Lawler, Ivan R.; Alldredge, Matthew W.

doi:10.2193/0022-541x(2006)70[255:eaaihe]2.0.co;2

Cited by 161 publications

(258 citation statements)

References 33 publications

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“…Our data also highlight the need to understand perception and availability bias when using observation based monitoring and citizen science for sea turtle monitoring (Pollock et al, 2006). In particular, we found that photo quality varied across species and was also likely to be influenced by depth (of the diver and turtle) and water visibility.…”

Section: Perception and Availability Biasmentioning

confidence: 60%

Spatial Distribution and Residency of Green and Loggerhead Sea Turtles Using Coastal Reef Habitats in Southern Mozambique

et al. 2017

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Sea turtles spend the majority of their immature and adult lives in foraging grounds, yet few studies have examined their abundance and condition in these areas when compared to more accessible nesting beach habitats. Here, a 5-year dive log, photo-identification (photo-ID) and surface encounter datasets were used to investigate the abundance, individual movements and distribution of sea turtles along 40 km of coastal reefs in southern Mozambique. A generalized linear model (GLM) was constructed with turtle sightings as the response variable. Habitat type, year and day of the year, as well as underwater visibility, were significant predictors of turtle sightings. However, only 8% of the total variance was explained by the model, indicating that other variables have a significant influence on turtle movement and distribution. Photo-ID differentiated 22 individual green turtles Chelonia mydas and 42 loggerhead turtles Caretta caretta from 323 photo-ID encounters. A majority (64%) of the photos could be used to identify the individual. Although residency times of up to 1152 days were calculated for juvenile green turtles, a low overall resighting rate indicates that individual turtles either had large home ranges or were transient to the area. Surface encounter data revealed a preference for nearshore shallow waters and an increased abundance close to reef systems. Sea turtles' preferences for shallow, nearshore habitats are likely to increase the encounter risk with opportunistic and targeted artisanal fishers who catch sea turtles.

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Section: Perception and Availability Biasmentioning

confidence: 60%

Spatial Distribution and Residency of Green and Loggerhead Sea Turtles Using Coastal Reef Habitats in Southern Mozambique

et al. 2017

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“…Cliff 2007) or occur in shallow areas and, providing water clarity is good enough, can be recorded while they are underwater (e.g. Pollock et al 2006). However, most marine species spend a considerable amount of time deeper in the water column and are thus not visible to an observer (not 'available').…”

Section: Distance Samplingmentioning

confidence: 99%

Monitoring marine populations and communities: methods dealing with imperfect detectability

Katsanevakis¹,

Weber²,

Pipitone³

et al. 2012

Aquat. Biol.

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Effective monitoring of populations and communities is a prerequisite for ecosystembased management of marine areas. However, monitoring programs often neglect important sources of error and thus can lead to biased estimates, spurious conclusions and false management actions. One such source of error is 'imperfect detectability', i.e. the inability of investigators to detect all individuals or all species in a surveyed area. Although there has been great effort to develop monitoring methods that account for imperfect detectability, the application of such methods in the marine environment is not as apparent as in other systems. Plot sampling is by far the most commonly applied method for biological monitoring in the marine environment, yet it largely ignores detectability issues. However, distance sampling, mark-recapture methods, repeated presence-absence surveys for occupancy estimation, and removal methods do estimate detection probabilities and provide unbiased estimates of state variables. We review these methods and the relevant tools for their application in studies on marine populations and communities, with the aim of assisting marine biologists and managers to understand the limitations and pitfalls associated with some approaches and to select the best available methods for their monitoring needs.

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“…A general conceptual model (Marsh and Sinclair 1989;Pollock et al 2004Pollock et al , 2006) for auditory count-based abundance estimates can be represented as:…”

Section: Introductionmentioning

confidence: 99%

Sources of Measurement Error, Misclassification Error, and Bias in Auditory Avian Point Count Data

Simons

Pollock²,

Wettroth³

et al. 2009

Modeling Demographic Processes in Marked Populations

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Avian point counts vary over space and time due to actual differences in abundance, differences in detection probabilities among counts, and differences associated with measurement and misclassification errors. However, despite the substantial time, effort, and money expended counting birds in ecological research and monitoring, the validity of common survey methods remains largely untested, and there is still considerable disagreement over the importance of estimating detection probabilities associated with individual counts. Most practitioners assume that current methods for estimating detection probability are accurate, and that observer training obviates the need to account for measurement and misclassification errors in point count data. Our approach combines empirical data from field studies with field experiments using a system for simulating avian census conditions when most birds are identified by sound. Our objectives are to: identify the factors that influence detection probability on auditory point counts, quantify the bias and precision of current sampling methods, and find new applications of sampling theory and methodologies that produce practical improvements in the quality of bird census data.We have found that factors affecting detection probabilities on auditory counts, such as ambient noise, can cause substantial biases in count data. Distance sampling data are subject to substantial measurement error due to the difficulty of estimating the distance to a sound source when visual cues are lacking. Misclassification errors are also inherent in time of detection methods due to the difficulty of accurately identifying and localizing sounds during a count. Factors affecting detection probability, measurement errors, and misclassification errors are important but often ignored components of the uncertainty associated with point-count-based abundance estimates.

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Estimating Animal Abundance in Heterogeneous Environments: An Application to Aerial Surveys for Dugongs

Cited by 161 publications

References 33 publications

Spatial Distribution and Residency of Green and Loggerhead Sea Turtles Using Coastal Reef Habitats in Southern Mozambique

Spatial Distribution and Residency of Green and Loggerhead Sea Turtles Using Coastal Reef Habitats in Southern Mozambique

Monitoring marine populations and communities: methods dealing with imperfect detectability

Sources of Measurement Error, Misclassification Error, and Bias in Auditory Avian Point Count Data

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