msocc: Fit and analyse computationally efficient multi‐scale occupancy models in <scp>r</scp>

Stratton, Christian; Sepulveda, Adam J.; Hoegh, Andrew

doi:10.1111/2041-210x.13442

Cited by 15 publications

(33 citation statements)

References 24 publications

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“…For example, a negative ESP result provided some confidence that the target species DNA was absent, while a 'no data' result from less frequent manual sampling provides no information. If resources are available to support frequent sampling, then either method would provide similar results, especially when integrated into a probabilistic framework for modeling uncertainty in the detection of the target species DNA 19 .…”

Section: Discussionmentioning

confidence: 99%

Robotic environmental DNA bio-surveillance of freshwater health

Sepulveda

Birch

Barnhart

et al. 2020

Sci Rep

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Autonomous water sampling technologies may help to overcome the human resource challenges of monitoring biological threats to rivers over long time periods and across large geographic areas. the Monterey Bay Aquarium Research institute has pioneered a robotic environmental Sample processor (eSp) that overcomes some of the constraints associated with traditional sampling since it can automate water sample filtration and preservation of the captured material. The ESP was originally developed for marine environment applications. Here we evaluated whether the eSp can provide reliable, timely information on environmental (e)DNA detections of human and fish pathogens and introduced fishes at U.S. Geological Survey streamgage sites in freshwater rivers. We compared eDNA collected via ESP at high frequency (e.g., every 3 h) with manual eDNA collections collected at lower frequency (e.g., weekly). We found that water samples filtered and preserved by ESPs successfully detected the DNA of human pathogens, fish pathogens and introduced fishes. Both ESP and manually collected samples provided similar information about target DnA presence. We suggest that the greatest current benefit of the ESP is the cost savings of high frequency, bio-surveillance at remote or hard to access sites. the full potential of robotic technologies like the eSp will be realized when they can more easily execute in situ analyses of water samples and rapidly transmit results to decision-makers. Invasive species have serious negative effects on regional and national economies 1,2. Thus, early detection is a central pillar of most monitoring programs because the earlier an invader is detected, the more likely control efforts will be effective in limiting invader spread and the resulting economic and environmental damages 3. Environmental DNA (eDNA) sampling has recently emerged as a sensitive, early detection tool because it can detect as little as a single cell from an invasive species by identifying the cellular or extracellular DNA that organisms release into the environment 4. Despite the ubiquity of eDNA in the aquatic environment, eDNA of targeted taxa is not always well mixed, so high detection probabilities often require intense sampling 5,6. Therefore, reliable detection requires trained individuals to manually collect water samples over long time periods or across large geographic areas 7. Autonomous, robotic water sampling technologies present an opportunity to overcome the temporal and human resource demands associated with eDNA sampling. Autonomous robots placed within the environment can conduct high frequency sampling, regardless of location, weather or the availability of human resources. The Monterey Bay Aquarium Research Institute (MBARI) has pioneered a robotic instrument called the Environmental Sample Processor (ESP) that overcomes the constraints of regular travel and work schedules, safety concerns with high water flows, and adverse weather. The ESP is a robotic device that can be programmed to automate water sample filtration an...

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Section: Discussionmentioning

confidence: 99%

Robotic environmental DNA bio-surveillance of freshwater health

Sepulveda

Birch

Barnhart

et al. 2020

Sci Rep

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“…Unlike previous R-packages for fitting multi-scale occupancy models that have been applied to eDNA data (Dorazio and Erickson, 2018; Stratton et al, 2020), our implementation of the Griffin et al (2019) model is novel in that it enables the estimation of false positive as well as false negative observation errors, both of which are known to be non-negligible in eDNA surveys. In addition, our RShiny app enables efficient Bayesian variable selection, which works well even when the number of predictors to be considered is large.…”

Section: Discussionmentioning

confidence: 99%

An Rshiny app for modelling environmental DNA data: accounting for false positive and false negative observation error

Diana

Matechou

Griffin

et al. 2020

Preprint

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Environmental DNA (eDNA) surveys have become a popular tool for assessing the distribution of species. However, it is known that false positive and false negative observation error can occur at both stages of eDNA surveys, namely the field sampling stage and laboratory analysis stage.We present an RShiny app that implements the Griffin et al. (2019) statistical method, which accounts for false positive and false negative errors in both stages of eDNA surveys. Following Griffin et al. (2019), we employ a Bayesian approach and perform efficient Bayesian variable selection to identify important predictors for the probability of species presence as well as the probabilities of observation error at either stage.We demonstrate the RShiny app using a data set on great crested newts collected by Natural England in 2018 and we identify water quality, pond area, fish presence, macrophyte cover, frequency of drying as important predictors for species presence at a site.The state-of-the-art statistical method that we have implemented is the only one that has specifically been developed for the purposes of modelling false negatives and false positives in eDNA data. Our RShiny app is user-friendly, requires no prior knowledge of R and fits the models very efficiently. Therefore, it should be part of the tool-kit of any researcher or practitioner who is collecting or analysing eDNA data.

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“…We used Bayesian multi-scale occupancy models in the msocc package (Stratton et al, 2020;R version 3.5.2) to estimate the detection probability of T. bryosalmonae and O. nerka DNA, to gain insight about covariates associated with eDNA detection probability, and to estimate the effort needed for confident and high-probability detection of target eDNA. These models allow for the analysis of occupancy with three levels of hierarchical sampling, while also accounting for false negatives in detection.…”

Section: Occupancy Analysesmentioning

confidence: 99%

“…Each data stream has nuances (e.g., unique attribute fields); however, the structure of eDNA data streams presents additional complications. This molecular method provides indirect inference about species presence, so occurrence probability must be modeled (Stratton et al, 2020). For eDNA analyses, multiple samples are collected at a location and multiple replicates from each sample are analyzed for the presence of the target organism DNA.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, each sample and replicate may detect the organism's DNA, if present, with some probability. Occupancy models provide a useful framework for the analysis of eDNA results (Stratton et al, 2020), in that these models account for the multiple nested levels of sampling that characterize eDNA surveys. Occupancy models also provide a useful framework for linking data streams describing abiotic conditions to those describing biological data, such as target taxa DNA (Pilliod et al, 2019;Sepulveda et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Integrating Environmental DNA Results With Diverse Data Sets to Improve Biosurveillance of River Health

Sepulveda

Hoegh

Gage³

et al. 2021

Front. Ecol. Evol.

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Autonomous, robotic environmental (e)DNA samplers now make it possible for biological observations to match the scale and quality of abiotic measurements collected by automated sensor networks. Merging these automated data streams may allow for improved insight into biotic responses to environmental change and stressors. Here, we merged eDNA data collected by robotic samplers installed at three U.S. Geological Survey (USGS) streamgages with gridded daily weather data, and daily water quality and quantity data into a cloud-hosted database. The eDNA targets were a rare fish parasite and a more common salmonid fish. We then used computationally expedient Bayesian hierarchical occupancy models to evaluate associations between abiotic conditions and eDNA detections and to simulate how uncertainty in result interpretation changes with the frequency of autonomous robotic eDNA sample collection. We developed scripts to automate data merging, cleaning and analysis steps into a chained-step, workflow. We found that inclusion of abiotic covariates only provided improved insight for the more common salmonid fish since its DNA was more frequently detected. Rare fish parasite DNA was infrequently detected, which caused occupancy parameter estimates and covariate associations to have high uncertainty. Our simulations found that collecting samples at least once per day resulted in more detections and less parameter uncertainty than less frequent sampling. Our occupancy and simulation results together demonstrate the advantages of robotic eDNA samplers and how these samples can be combined with easy to acquire, publicly available data to foster real-time biosurveillance and forecasting.

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msocc: Fit and analyse computationally efficient multi‐scale occupancy models in r

Cited by 15 publications

References 24 publications

Robotic environmental DNA bio-surveillance of freshwater health

Robotic environmental DNA bio-surveillance of freshwater health

An Rshiny app for modelling environmental DNA data: accounting for false positive and false negative observation error

Integrating Environmental DNA Results With Diverse Data Sets to Improve Biosurveillance of River Health

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