575 items found
Type:
Subjects:
Description:Species covered: deer, turkey, waterfowl, small game, fishing
Colorado's wildlife officer's pride themselves on knowing their districts better than anyone else. This knowledge is invaluable for hunters trying to make decisions on where to apply and/or start scouting. In an inclusive effort to better inform hunters, we're happy to be the first state in the nation to offer authentic online content straight from the local game warden's mouth! No script. No wannabe experts. Each officer brings to light answers to the most often asked questions they receive in their district, including access and issues that are germane to that specific portion of Colorado.
Wildlife Officers sometimes transfer districts and for the most updated contact information, hunters should look at the Colorado Hunting Atlas or call the local CPW office.
Buy hunting and fishing licenses: https://www.cpwshop.com
Denver: 303-291-7227
Fort Collins: 970-472-4300
Brush: 970-842-6300
Full list of regional and area offices: https://cpw.state.co.us/aboutus/Pages/ContactUs.aspx
Colorado Hunting Atlas: https://ndismaps.nrel.colostate.edu/index.html?app=HuntingAtlas
Learn to Hunt Video Series: https://www.youtube.com/playlist?list=PLWGY7bVNQHtWLZQuFJtW-PUFGEIjpgec4
Hunting Regulations Brochures:
https://cpw.state.co.us/aboutus/Pages/RegulationsBrochures.aspx [show more]
Description:Species covered: Mule Deer, Whitetail Deer, Pheasants, Turkey, Walleye Fishing
Colorado's wildlife officer's pride themselves on knowing their districts better than anyone else. This knowledge is invaluable for hunters trying to make decisions on where to apply and/or start scouting. In an inclusive effort to better inform hunters, we're happy to be the first state in the nation to offer authentic online content straight from the local game warden's mouth! No script. No wannabe experts. Each officer brings to light answers to the most often asked questions they receive in their district, including access and issues that are germane to that specific portion of Colorado.
Wildlife Officers sometimes transfer districts and for the most updated contact information, hunters should look at the Colorado Hunting Atlas or call the local CPW office.
Buy hunting and fishing licenses: https://www.cpwshop.com
Denver: 303-291-7227
Fort Collins: 970-472-4300
Brush: 970-842-6300
Full list of regional and area offices: https://cpw.state.co.us/aboutus/Pages/ContactUs.aspx
Colorado Hunting Atlas: https://ndismaps.nrel.colostate.edu/index.html?app=HuntingAtlas
Learn to Hunt Video Series: https://www.youtube.com/playlist?list=PLWGY7bVNQHtWLZQuFJtW-PUFGEIjpgec4
Hunting Regulations Brochures:
https://cpw.state.co.us/aboutus/Pages/RegulationsBrochures.aspx [show more]
Description:Species covered: Mule Deer, Whitetail, Pronghorn, Waterfowl, Turkey, Small Game
Colorado's wildlife officer's pride themselves on knowing their districts better than anyone else. This knowledge is invaluable for hunters trying to make decisions on where to apply and/or start scouting. In an inclusive effort to better inform hunters, we're happy to be the first state in the nation to offer authentic online content straight from the local game warden's mouth! No script. No wannabe experts. Each officer brings to light answers to the most often asked questions they receive in their district, including access and issues that are germane to that specific portion of Colorado.
Wildlife Officers sometimes transfer districts and for the most updated contact information, hunters should look at the Colorado Hunting Atlas or call the local CPW office.
Buy hunting and fishing licenses: https://www.cpwshop.com
Denver: 303-291-7227
Fort Collins: 970-472-4300
Brush: 970-842-6300
Full list of regional and area offices: https://cpw.state.co.us/aboutus/Pages/ContactUs.aspx
Colorado Hunting Atlas: https://ndismaps.nrel.colostate.edu/index.html?app=HuntingAtlas
Learn to Hunt Video Series: https://www.youtube.com/playlist?list=PLWGY7bVNQHtWLZQuFJtW-PUFGEIjpgec4
Hunting Regulations Brochures:
https://cpw.state.co.us/aboutus/Pages/RegulationsBrochures.aspx [show more]
Type:
Subjects:
Description:Species covered: Mule Deer, Whitetails
Colorado's wildlife officer's pride themselves on knowing their districts better than anyone else. This knowledge is invaluable for hunters trying to make decisions on where to apply and/or start scouting. In an inclusive effort to better inform hunters, we're happy to be the first state in the nation to offer authentic online content straight from the local game warden's mouth! No script. No wannabe experts. Each officer brings to light answers to the most often asked questions they receive in their district, including access and issues that are germane to that specific portion of Colorado.
Wildlife Officers sometimes transfer districts and for the most updated contact information, hunters should look at the Colorado Hunting Atlas or call the local CPW office.
Buy hunting and fishing licenses: https://www.cpwshop.com
Denver: 303-291-7227
Fort Collins: 970-472-4300
Brush: 970-842-6300
Full list of regional and area offices: https://cpw.state.co.us/aboutus/Pages/ContactUs.aspx
Colorado Hunting Atlas: https://ndismaps.nrel.colostate.edu/index.html?app=HuntingAtlas
Learn to Hunt Video Series: https://www.youtube.com/playlist?list=PLWGY7bVNQHtWLZQuFJtW-PUFGEIjpgec4
Hunting Regulations Brochures:
https://cpw.state.co.us/aboutus/Pages/RegulationsBrochures.aspx [show more]
Type:Fact Sheet
Subject:Wildlife diseases
Echinococcosis
Echinococcosis
Description:Fact sheet covering species affected in Colorado, what to look for, cause and transmission, and public health considerations.
Type:
Subjects:
Description:
Led ByChad Bishop
Study AreaUncompahgre Plateau
Project StatusCompleted, 2005
Research Objectives
- To evaluate the importance of habitat quality on mule deer population dynamics.
- To determine management priorities to reverse mule deer population declines.
Project Description
In the 1990s, mule deer populations began to decline across the western United States. CPW researchers set out to identify reasons for the decline, focusing on those factors that could be controlled through management efforts. Researchers recognized both habitat quality and predation as possible factors, but did not know which one played a larger role in mule deer population declines.
To evaluate habitat as a limiting factor, researchers measured the effect of habitat enhancements on mule deer survival and fawn recruitment during a six-year study.
During the winter months, researchers artificially enhanced habitat quality by distributing feed supplement pellets around the study area to improve deer nutrition. Predation levels were left unchanged. Researchers then measured pregnancy rates, doe body condition, and doe and fawn survival rates in the supplemented area and in an un-supplemented control area. Halfway through the study, the treatment and control areas were reversed in a crossover design.
Fawn survival in the supplemented area was significantly greater than in the control area during the winter, which resulted in a positive rate of population increase. These results provided clear evidence that nutrition and habitat quality were important factors contributing to deer population declines.
This CPW project aligned with a research project conducted by the Idaho Fish and Game Department, which found that predator control had a smaller positive effect on declining mule deer populations.
Although completed in 2005, this project still serves as a reference and basis for many of CPW's current research projects. This project also helped wildlife managers determine factors that could be managed to reverse mule deer population declines.
Resources
West Slope Mule Deer StrategyBishop, Chad J. and Gary C. White. 2001-2006. Effect of nutrition and habitat enhancements on mule deer recruitment and survival rates. Colorado Division of Wildlife. (Compiled progress reports from a Federal Aid project).
[show more]Type: Article
Subjects: Canada lynx
Generalizability
GPS telemetry data
Local adaptation
Niche similarity
Regional variation
Sample size
Species distribution model
Transferability
Generalizability
GPS telemetry data
Local adaptation
Niche similarity
Regional variation
Sample size
Species distribution model
Transferability
Type:Article
Subject:Canada lynx
Generalizability
GPS telemetry data
Local adaptation
Niche similarity
Regional variation
Sample size
Species distribution model
Transferability
Generalizability
GPS telemetry data
Local adaptation
Niche similarity
Regional variation
Sample size
Species distribution model
Transferability
Description:The application of species distribution models (SDMs) to areas outside of where a model was created allows informed decisions across large spatial scales, yet transferability remains a challenge in ecological modeling. We examined how regional variation in animal-environment relationships influenced model transferability for Canada lynx (Lynx canadensis), with an additional conservation aim of modeling lynx habitat across the northwestern United States. Simultaneously, we explored the effect of sample size from GPS data on SDM model performance and transferability. We used data from three geographically distinct Canada lynx populations in Washington (n = 17 individuals), Montana (n = 66), and Wyoming (n = 10) from 1996 to 2015. We assessed regional variation in lynx-environment relationships between these three populations using principal components analysis (PCA). We used ensemble modeling to develop SDMs for each population and all populations combined and assessed model prediction and transferability for each model scenario using withheld data and an extensive independent dataset (n = 650). Finally, we examined GPS data efficiency by testing models created with sample sizes of 5%–100% of the original datasets. PCA results indicated some differences in environmental characteristics between populations; models created from individual populations showed differential transferability based on the populations' similarity in PCA space. Despite population differences, a single model created from all populations performed as well, or better, than each individual population. Model performance was mostly insensitive to GPS sample size, with a plateau in predictive ability reached at ~30% of the total GPS dataset when initial sample size was large. Based on these results, we generated well-validated spatial predictions of Canada lynx distribution across a large portion of the species' southern range, with precipitation and temperature the primary environmental predictors in the model. We also demonstrated substantial redundancy in our large GPS dataset, with predictive performance insensitive to sample sizes above 30% of the original. [show more]
Type: Article
Subjects: Geographic Information Systems (GIS)
Quality Improvement
Animal Distribution
Cluster Analysis
Puma
Research
Animal behavior
Global positioning systems (GPS)
Mathematical models
Telemetry
Quality Improvement
Animal Distribution
Cluster Analysis
Puma
Research
Animal behavior
Global positioning systems (GPS)
Mathematical models
Telemetry
Type:Article
Subject:Geographic Information Systems (GIS)
Quality Improvement
Animal Distribution
Cluster Analysis
Puma
Research
Animal behavior
Global positioning systems (GPS)
Mathematical models
Telemetry
Quality Improvement
Animal Distribution
Cluster Analysis
Puma
Research
Animal behavior
Global positioning systems (GPS)
Mathematical models
Telemetry
Description:Animal space use studies using GPS collar technology are increasingly incorporating behavior based analysis of spatio-temporal data in order to expand inferences of resource use. GPS location cluster analysis is one such technique applied to large carnivores to identify the timing and location of feeding events. For logistical and financial reasons, researchers often implement predictive models for identifying these events. We present two separate improvements for predictive models that future practitioners can implement. Thus far, feeding prediction models have incorporated a small range of covariates, usually limited to spatio-temporal characteristics of the GPS data. Using GPS collared cougar (Puma concolor) we include activity sensor data as an additional covariate to increase prediction performance of feeding presence/absence. Integral to the predictive modeling of feeding events is a ground-truthing component, in which GPS location clusters are visited by human observers to confirm the presence or absence of feeding remains. Failing to account for sources of ground-truthing false-absences can bias the number of predicted feeding events to be low. Thus we account for some ground-truthing error sources directly in the model with covariates and when applying model predictions. Accounting for these errors resulted in a 10% increase in the number of clusters predicted to be feeding events. Using a double-observer design, we show that the ground-truthing false-absence rate is relatively low (4%) using a search delay of 2–60 days. Overall, we provide two separate improvements to the GPS cluster analysis techniques that can be expanded upon and implemented in future studies interested in identifying feeding behaviors of large carnivores. [show more]
Type: Article
Subjects: Computer vision
Deep convolutional neural networks
Image classification
Machine learning
Motion-activated camera
R package
Remote sensing
Species identification
Deep convolutional neural networks
Image classification
Machine learning
Motion-activated camera
R package
Remote sensing
Species identification
Type:Article
Subject:Computer vision
Deep convolutional neural networks
Image classification
Machine learning
Motion-activated camera
R package
Remote sensing
Species identification
Deep convolutional neural networks
Image classification
Machine learning
Motion-activated camera
R package
Remote sensing
Species identification
Description:Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths. [show more]
Type: Article
Subjects: Colorado
Demography
Fawn ratios
Land-use change
Odocoileus hemionus
Residential development
Weather
Winter range
Demography
Fawn ratios
Land-use change
Odocoileus hemionus
Residential development
Weather
Winter range
Type:Article
Subject:Colorado
Demography
Fawn ratios
Land-use change
Odocoileus hemionus
Residential development
Weather
Winter range
Demography
Fawn ratios
Land-use change
Odocoileus hemionus
Residential development
Weather
Winter range
Description:Land-use change due to anthropogenic development is pervasive across the globe and commonly associated with negative consequences for biodiversity. While land-use change has been linked to shifts in the behavior and habitat-use patterns of wildlife species, little is known about its influence on animal population dynamics, despite the relevance of such information for conservation. We conducted the first broad-scale investigation correlating temporal patterns of land-use change with the demographic rates of mule deer, an iconic species in the western United States experiencing wide-scale population declines. We employed a unique combination of long-term (1980–2010) data on residential and energy development across western Colorado, in conjunction with congruent data on deer recruitment, to quantify annual changes in land-use and correlate those changes with annual indices of demographic performance. We also examined annual variation in weather conditions, which are well recognized to influence ungulate productivity, and provided a basis for comparing the relative strength of different covariates in their association with deer recruitment. Using linear mixed models, we found that increasing residential and energy development within deer habitat were correlated with declining recruitment rates, particularly within seasonal winter ranges. Residential housing had two times the magnitude of effect of any other factor we investigated, and energy development had an effect size similar to key weather variables known to be important to ungulate dynamics. This analysis is the first to correlate a demographic response in mule deer with residential and energy development at large spatial extents relevant to population performance, suggesting that further increases in these development types on deer ranges are not compatible with the goal of maintaining highly productive deer populations. Our results underscore the significance of expanding residential development on mule deer populations, a factor that has received little research attention in recent years, despite its rapidly increasing footprint across the landscape. [show more]