Effects of scale of movement, detection probability, and true population density on common methods of estimating population density

Keiter, David A.; Davis, Amy J.; Rhodes, Olin E.; Cunningham, Fred L.; Kilgo, John C.; Pepin, Kim M.; Beasley, James C.

doi:10.1038/s41598-017-09746-5

Cited by 53 publications

(58 citation statements)

References 39 publications

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“…Using more detectors also reduced variability across trials, implying that less effort is required to obtain accurate density estimates. Sampling designs should seek to maximize the number of encounters with a target species, using a number and spacing of detectors that capture the movement of the species of interest, while maintaining independence across detectors (Keiter et al 2017).…”

Section: Discussionmentioning

confidence: 99%

Estimating animal density in three dimensions using capture‐frequency data from remote detectors

Soto

Castañeda

Mandrak

et al. 2020

Remote Sens Ecol Conserv

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Remote detectors are being used increasingly often to study aquatic and aerial species, which move significantly differently from terrestrial species. Camera-trapping studies have used frameworks based on animal movement patterns to show that a species' detection frequency, along with movement speed and detector specifications, can be used to estimate absolute population density. This approach, however, has not yet been adapted to cases where movement is threedimensional. Here we adapt one such framework to three-dimensional movement, to characterize the relationship between population density, animal speed, characteristics of a remote sensor's detection zone and detection frequency. The derivation involves defining the detection zone mathematically and calculating the mean area of the profile it presents to approaching individuals. We developed two variants of the model-one assuming random movement of all individuals, and one allowing for different probabilities for each approach direction (e.g. that animals more often swim/fly horizontally than vertically). We used computer simulations to evaluate model performance for a wide range of animal and detector densities. Simulations show that in ideal conditions the method approximates true density well, and that estimates become increasingly accurate using more detectors, or sampling for longer. Moreover the method is robust to violations of assumptions, accuracy is decreased only in extreme cases where all detectors are facing the same way. We provide equations for estimating population density from detection frequency and outline how to estimate the necessary parameters. We discuss how environmental variables and species-specific characteristics affect parameter estimates and how to account for these differences in density estimations. Our method can be applied to common remote detection methods (cameras and acoustic detectors), which are currently being used to study a diversity of species and environments. Therefore, our work may significantly expand the number and diversity of species for which density can be estimated.

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Section: Discussionmentioning

confidence: 99%

Estimating animal density in three dimensions using capture‐frequency data from remote detectors

Soto

Castañeda

Mandrak

et al. 2020

Remote Sens Ecol Conserv

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“…This program is managed by USFS and currently removes ~ 1,300-1,800 pigs annually [28]. Despite this control, there are several thousand wild pigs inhabiting the SRS that are distributed throughout the site [29]. Since the SRS was previously used to manufacture nuclear materials and manage nuclear waste [30], there is limited public access across the site.…”

Section: Study Areamentioning

confidence: 99%

“…Female wild pigs selected all vegetation types (i.e., upland pines, upland hardwoods, bottomland hardwoods, shrub/herbaceous) across our study area in their home-range placement at the second order in both the low and high-forage seasons (Fig. 4), likely re ecting the ubiquitous establishment of wild pigs across the Savannah River Site [29]. Females also selected locations closer to streams and avoided areas near roads.…”

Section: Resource Selectionmentioning

confidence: 99%

Connecting the Dots: Behavioral State Resource Selection in Wild Pigs in the Southeast United States

Clontz¹,

Pepin²,

VerCauteren³

et al. 2020

Preprint

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Background Invasive wild pigs (Sus scrofa) are behavioral generalists that have the potential to alter ecosystems across broad spatial scales. Elucidating the correlation between wild pig behavior and landscape attributes can aid in the advancement of management strategies for controlling populations. Methods Using GPS data from 49 wild pigs in the southeastern U.S., we used movement characteristics to distinguish and define behaviors and explore the connection between these behaviors and resource selection for both females and males between two distinct seasons based on forage availability. We extracted three biologically relevant behaviors of wild pigs from our movement data using hidden Markov models, which we inferred to represent resting, foraging, and traveling behavioral states. We then used multi-scale resource selection functions to quantify resource selection at the population and home-range scales for each behavioral state. Results Females demonstrated a crepuscular movement pattern in the high-forage season and a variable pattern in the low-forage season, with increased traveling and foraging activity during the daytime and evening hours, while males consistently demonstrated a nocturnal pattern across both seasons. At the population scale, wild pigs selected for forest vegetation types (i.e., upland pines, upland hardwoods, bottomland hardwoods) in both the low- and high-forage seasons, likely reflecting their ubiquitous establishment throughout the landscape. At the home-range scale, wild pigs selected for bottomland hardwoods and dense canopy cover in all behavioral states. In addition, males demonstrated selection for a variety of vegetation types while foraging in the low-forage season compared to the high-forage season and demonstrated an increased use of linear anthropogenic features (e.g., roads) across seasons while traveling. Conclusions Our results demonstrate male and female pigs exhibit clear differences in movement behavior. Further, although wild pigs can establish populations and home ranges in an array of landscapes and habitat types, there are key resources associated with common behaviors they select consistently at a fine scale that can be targeted in conservation and management programs across their invasive range.

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“…Non-invasive genetic mark-recapture methods were found to be biased high compared to camera trap mark-recapture methods as age differentials were not possible with the genetic methods 28 . Removal models have been shown to be biased when sample sizes are low 29 . Without considering costs, the optimal method may be dependent on unique conditions for a study system of interest.…”

mentioning

confidence: 99%

A comparison of cost and quality of three methods for estimating density for wild pig (Sus scrofa)

Davis

Keiter

Kierepka

et al. 2020

Sci Rep

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A critical element in effective wildlife management is monitoring the status of wildlife populations; however, resources to monitor wildlife populations are typically limited. We compared cost effectiveness of three common population estimation methods (i.e. non-invasive DNA sampling, camera sampling, and sampling from trapping) by applying them to wild pigs (Sus scrofa) across three habitats in South Carolina, U.S.A where they are invasive. We used mark-recapture analyses for fecal DNA sampling data, spatially-explicit capture-recapture analyses for camera sampling data, and a removal analysis for removal sampling from trap data. Density estimates were similar across methods. Camera sampling was the least expensive, but had large variances. Fecal DNA sampling was the most expensive, although this technique generally performed well. We examined how reductions in effort by method related to increases in relative bias or imprecision. for removal sampling, the largest cost savings while maintaining unbiased density estimates was from reducing the number of traps. for fecal DNA sampling, a reduction in effort only minimally reduced costs due to the need for increased lab replicates while maintaining high quality estimates. For camera sampling, effort could only be marginally reduced before inducing bias. We provide a decision tree for researchers to help make monitoring decisions.

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Effects of scale of movement, detection probability, and true population density on common methods of estimating population density

Cited by 53 publications

References 39 publications

Estimating animal density in three dimensions using capture‐frequency data from remote detectors

Estimating animal density in three dimensions using capture‐frequency data from remote detectors

Connecting the Dots: Behavioral State Resource Selection in Wild Pigs in the Southeast United States

A comparison of cost and quality of three methods for estimating density for wild pig (Sus scrofa)

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