Large-scale variation in density of an aquatic ecosystem indicator species

Sutherland, Chris; Fuller, Angela K.; Royle, J. Andrew; Hare, Matthew P.; Madden, Sean

doi:10.1038/s41598-018-26847-x

Cited by 25 publications

(19 citation statements)

References 49 publications

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“…Recently, Sutherland et al. () examined the link between contamination (PCB) and the population density of American mink ( Neovison vison ) using genetic‐based SCR. Mink, often used as an indicator species for aquatic ecosystems, were found in much lower densities near a contaminated river system compared to those near a more pristine river; underscoring the value of pairing SCR methods with genetic tags to collect individual identities across multiple river systems to reveal cryptic ecological patterns.…”

Section: Introductionmentioning

confidence: 99%

Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems

Lamb

Ford

Proctor³

et al. 2019

Ecological Applications

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The Anthropocene is an era of marked human impact on the world. Quantifying these impacts has become central to understanding the dynamics of coupled human‐natural systems, resource‐dependent livelihoods, and biodiversity conservation. Ecologists are facing growing pressure to quantify the size, distribution, and trajectory of wild populations in a cost‐effective and socially acceptable manner. Genetic tagging, combined with modern computational and genetic analyses, is an under‐utilized tool to meet this demand, especially for wide‐ranging, elusive, sensitive, and low‐density species. Genetic tagging studies are now revealing unprecedented insight into the mechanisms that control the density, trajectory, connectivity, and patterns of human–wildlife interaction for populations over vast spatial extents. Here, we outline the application of, and ecological inferences from, new analytical techniques applied to genetically tagged individuals, contrast this approach with conventional methods, and describe how genetic tagging can be better applied to address outstanding questions in ecology. We provide example analyses using a long‐term genetic tagging dataset of grizzly bears in the Canadian Rockies. The genetic tagging toolbox is a powerful and overlooked ensemble that ecologists and conservation biologists can leverage to generate evidence and meet the challenges of the Anthropocene.

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Section: Introductionmentioning

confidence: 99%

Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems

Lamb

Ford

Proctor³

et al. 2019

Ecological Applications

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“…It is worth noting that the designs produced using this framework can be considered approximate in terms of specific location, and that the actual, finer-scale site-selection for traps can be informed by knowledge of the species’ biology and behavior (e.g., Fabiano et al, 2020). Further, while we develop this framework with camera traps in mind, this method can easily be applied to determine the general location of other non-invasive surveys, wherein the selection of a sampling location instead activates some other form of sampling effort (see Fuller et al 2016; Sutherland et al 2018). Importantly, the degree of sampling effort must be maintained among all selected sampling locations.…”

Section: Discussionmentioning

confidence: 99%

Optimal sampling design for spatial capture-recapture

Dupont

Royle

Nawaz

et al. 2020

Preprint

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10Spatial capture-recapture (SCR) has emerged as the industry standard for 11 analyzing observational data to estimate population size by leveraging information from 12 spatial locations of repeat encounters of individuals. The resulting precision of density 13 estimates depends fundamentally on the number and spatial configuration of traps.14 Despite this knowledge, existing sampling design recommendations are heuristic and 15 their performance remains untested for most practical applications -i.e., 16spatially-structured and logistically challenging landscapes. To address this issue, we 17 propose a genetic algorithm that minimizes any sensible, criteria-based objective 18 function to produce near-optimal sampling designs. To motivate the idea of optimality, 19 we compare the performance of designs optimized using two model-based criteria 20 related to the probability of capture. We use simulation to show that these designs 21 out-perform those based on existing recommendations in terms of bias, precision, and 22 accuracy in the estimation of population size. Our approach allows conservation 23 practitioners and researchers to generate customized sampling designs that can improve 24 monitoring of wildlife populations. 25Keywords-SCR, spatial capture-recapture, spatially-explicit capture-recapture, 26 camera traps, density, optimal design, sampling design, spatial sampling, trap spacing 27 29 (Williams et al., 2002) which has driven the development of data collection and estimation 30 methods, especially those that can account for imperfect detection. Capture-recapture (CR), 31 and more recently, spatial capture-recapture (SCR: Royle et al., 2014) methods were 32 developed specifically for this purpose and are now routinely applied in ecological research. 33 Concurrently, SCR methods estimate detection, space use, and density by analyzing 34 2 individual encounter histories while explicitly incorporating auxiliary information from the 35 spatial organization of encounters (Efford, 2004; Royle et al., 2014). Despite widespread 36 adoption and rapid method development, recommendations about spatial sampling design 37 have received relatively little attention and are arguably heuristic. 38 The effects of sampling design have been investigated for both CR (Dillon and Kelly 39 2007; Bondrup-Nielsen 1983; Gardner et al. 2010) and SCR methods (discussed in the next 40 paragraph). While CR methods aim to balance the number of captures and the number of 41 recaptures, SCR requires a third consideration, the number of spatial recaptures, i.e., the 42 number of times individuals are observed at multiple locations. The ability to reliably 43 estimate these quantities is directly related to the quality of the data collected: the number of 44 captured individuals n is the sample size; the number of recaptures is directly related to the 45 baseline detection probability, g 0 ; and the number and spatial distribution of recaptures are 46 directly related to the spatial scale parameter, σ. Therefore, improving sampling design...

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“…Finally, our movement-assisted localization framework provides a host of opportunities to evaluate integrated, multi-objective study designs. In general, the design of receiver arrays can be based on objectives that involve maximizing the probability of detecting a tagged individual or statistical precision of detection parameters [18,42]. Combining a state-space movement model with a sub-model for the detection process (i.e., a positioning model; [17]) dramatically improved localization precision, whereby only 1 -2 detections per occasion resulted in similar precision as 5 -6 detections in the independent localization model.…”

Section: Discussionmentioning

confidence: 99%

Movement-assisted localization from acoustic telemetry data

Hostetter

Royle

2020

Mov Ecol

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Background: Acoustic telemetry technologies are being increasingly deployed to study a variety of aquatic taxa including fishes, reptiles, and marine mammals. Large cooperative telemetry networks produce vast quantities of data useful in the study of movement, resource selection and species distribution. Efficient use of acoustic telemetry data requires estimation of acoustic source locations from detections at receivers (i.e., "localization"). Multiple processes provide information for localization estimation including detection/non-detection data at receivers, information on signal rate, and an underlying movement model describing how individuals move and utilize space. Frequently, however, localization methods only integrate a subset of these processes and do not utilize the full spatial encounter history information available from receiver arrays. Methods: In this paper we draw analogies between the challenges of acoustic telemetry localization and newly developed methods of spatial capture-recapture (SCR). We develop a framework for localization that integrates explicit sub-models for movement, signal (or cue) rate, and detection probability, based on acoustic telemetry spatial encounter history data. This method, which we call movement-assisted localization, makes efficient use of the full encounter history data available from acoustic receiver arrays, provides localizations with fewer than three detections, and even allows for predictions to be made of the position of an individual when it was not detected at all. We demonstrate these concepts by developing generalizable Bayesian formulations of the SCR movement-assisted localization model to address study-specific challenges common in acoustic telemetry studies. Results: Simulation studies show that movement-assisted localization models improve point-wise RMSE of localization estimates by > 50% and greatly increased the precision of estimated trajectories compared to localization using only the detection history of a given signal. Additionally, integrating a signal rate sub-model reduced biases in the estimation of movement, signal rate, and detection parameters observed in independent localization models. Conclusions: Movement-assisted localization provides a flexible framework to maximize the use of acoustic telemetry data. Conceptualizing localization within an SCR framework allows extensions to a variety of data collection protocols, improves the efficiency of studies interested in movement, resource selection, and space-use, and provides a unifying framework for modeling acoustic data.

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Large-scale variation in density of an aquatic ecosystem indicator species

Cited by 25 publications

References 49 publications

Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems

Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems

Optimal sampling design for spatial capture-recapture

Movement-assisted localization from acoustic telemetry data

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