Official state watercraft inspection and decontamination (WID) protocols and procedures | Official state watercraft inspection and decontamination (WID) protocols and procedures | Text | Aquatic nuisance species ANS Aquatic invasive species AIS Boating Inspection |
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Type:Text Subject:Aquatic nuisance species ANS Aquatic invasive species AIS Boating Inspection |
Description:Aquatic nuisance species (ANS) or aquatic invasive species (AIS) are also called non-native species, exotic species, non-indigenous species, noxious weeds, or pests. ANS can be plants or animals. Invasive aquatic plants are introduced plants that live either partially or completely submerged in the water and out-compete native species for light, space and nutrients creating a dense monoculture. Invasive aquatic animals also outcompete native species and require a watery habitat, but do not necessarily have to live entirely in water. [show more]
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Scuba divers can help stop aquatic hitchhikers | Scuba divers can help stop aquatic hitchhikers | Brochure | Aquatic nuisance species ANS Scuba apparatus Scuba diving |
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Type:Brochure Subject:Aquatic nuisance species ANS Scuba apparatus Scuba diving |
Description:Divers can unintentionally spread freshwater aquatic nuisance species (ANS) such as the zebra or quagga mussel, New Zealand Mudsnail (NZMS), Eurasian watermilfoil, spiny water flea, or Asian clam from one body of water to another on their scuba diving gear. Some ANS larvae are invisible to the naked eye and can survive hours to weeks on wet scuba gear or in water inside equipment. By adhering to the following guidelines, you can help prevent the spread of ANS when you scuba dive. [show more]
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Zebra and quagga mussel management plan | Zebra and quagga mussel management plan | Text | Aquatic nuisance species ANS Zebra mussels Quagga mussels |
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Type:Text Subject:Aquatic nuisance species ANS Zebra mussels Quagga mussels |
Description:The Colorado Zebra/Quagga Mussel Management Plan (ZQM Plan) outlines a statewide collaborative effort to detect, contain, and substantially reduce the risk of the spread and further infestation by zebra/quagga mussels in Colorado. The Plan is coordinated by the Colorado Division of Wildlife (CDOW) as part of the State Aquatic Nuisance Species (ANS) Program. The Plan’s primary components are early detection and rapid response, containment, prevention and education/outreach. [show more]
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CPW Aquatic research publications, reports, and presentations 2020-2021 | CPW Aquatic research publications, reports, and presentations 2020-2021 | Bibliography | Aquatic research |
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Type:Bibliography Subject:Aquatic research |
Description:Aquatic research publications, reports, and presentations
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CPW Aquatic research publications, reports, and presentations 2021-2022 | CPW Aquatic research publications, reports, and presentations 2021-2022 | Text | Aquatic research |
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Type:Text Subject:Aquatic research |
Description:Aquatic research publications, reports, and presentations
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Scientific solutions for fisheries management | Scientific solutions for fisheries management | | Aquatic research Fisheries management |
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Subject:Aquatic research Fisheries management |
Description:An overview of the Aquatic Research Section and how the researchers use a science-based approach to their work
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A functional model for characterizing long-distance movement behaviour | A functional model for characterizing long-distance movement behaviour | Article | Argos Bayesian model Canada lynx Functional data analysis Movement modelling Splines Telemetry |
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Type:Article Subject:Argos Bayesian model Canada lynx Functional data analysis Movement modelling Splines Telemetry |
Description: Summary
- Advancements in wildlife telemetry techniques have made it possible to collect large data sets of highly accurate animal locations at a fine temporal resolution. These data sets have prompted the development of a number of statistical methodologies for modelling animal movement.
- Telemetry data sets are often collected for purposes other than fine-scale movement analysis. These data sets may differ substantially from those that are collected with technologies suitable for fine-scale movement modelling and may consist of locations that are irregular in time, are temporally coarse or have large measurement error. These data sets are time-consuming and costly to collect but may still provide valuable information about movement behaviour.
- We developed a Bayesian movement model that accounts for error from multiple data sources as well as movement behaviour at different temporal scales. The Bayesian framework allows us to calculate derived quantities that describe temporally varying movement behaviour, such as residence time, speed and persistence in direction. The model is flexible, easy to implement and computationally efficient.
- We apply this model to data from Colorado Canada lynx (Lynx canadensis) and use derived quantities to identify changes in movement behaviour.
[show more]
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Machine learning to classify animal species in camera trap images: applications in ecology | Machine learning to classify animal species in camera trap images: applications in ecology | Article | Artificial intelligence Camera trap Convolutional neural network Deep neural networks Image classification Machine learning r package Remote sensing |
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Type:Article Subject:Artificial intelligence Camera trap Convolutional neural network Deep neural networks Image classification Machine learning r package Remote sensing |
Description:
- Motion-activated cameras (“camera traps”) are increasingly used in ecological and management studies for remotely observing wildlife and are amongst the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analysed, typically by visually observing each image, in order to extract data that can be used in ecological analyses.
- We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or “out-of-distribution” in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model.
- The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an r package (Machine Learning for Wildlife Image Classification) that allows the users to (a) use the trained model presented here and (b) train their own model using classified images of wildlife from their studies.
- The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analysing images. Our r package makes these methods accessible to ecologists.
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A field evaluation of the effectiveness of distance sampling and double independent observers to estimate detection probability in aural avian point counts | A field evaluation of the effectiveness of distance sampling and double independent observers to estimate detection probability in aural avian point counts | Article | Aural detections Availability process Avian point counts Detection probability Field tests Perception process Time-of-detection method |
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Type:Article Subject:Aural detections Availability process Avian point counts Detection probability Field tests Perception process Time-of-detection method |
Description:The time-of-detection method for aural avian point counts is a new method of estimating abundance, allowing for uncertain probability of detection. The method has been specifically designed to allow for variation in singing rates of birds. It involves dividing the time interval of the point count into several subintervals and recording the detection history of the subintervals when each bird sings. The method can be viewed as generating data equivalent to closed capture–recapture information. The method is different from the distance and multiple-observer methods in that it is not required that all the birds sing during the point count. As this method is new and there is some concern as to how well individual birds can be followed, we carried out a field test of the method using simulated known populations of singing birds, using a laptop computer to send signals to audio stations distributed around a point. The system mimics actual aural avian point counts, but also allows us to know the size and spatial distribution of the populations we are sampling. Fifty 8-min point counts (broken into four 2-min intervals) using eight species of birds were simulated. Singing rate of an individual bird of a species was simulated following a Markovian process (singing bouts followed by periods of silence), which we felt was more realistic than a truly random process. The main emphasis of our paper is to compare results from species singing at (high and low) homogenous rates per interval with those singing at (high and low) heterogeneous rates. Population size was estimated accurately for the species simulated, with a high homogeneous probability of singing. Populations of simulated species with lower but homogeneous singing probabilities were somewhat underestimated. Populations of species simulated with heterogeneous singing probabilities were substantially underestimated. Underestimation was caused by both the very low detection probabilities of all distant individuals and by individuals with low singing rates also having very low detection probabilities. [show more]
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Practical guidance on characterizing availability in resource selection functions under a use–availability design | Practical guidance on characterizing availability in resource selection functions under a use–availability design | Article | Autocorrelation GPS radio telemetry Resource selection function (RSF) Spatial point process Species distribution model Use–availability data Wildlife |
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Type:Article Subject:Autocorrelation GPS radio telemetry Resource selection function (RSF) Spatial point process Species distribution model Use–availability data Wildlife |
Description:Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use–availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use–availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness. [show more]
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