Electronic tracking provides a unique way to document behaviour by cows on a continuous basis. Ov... more Electronic tracking provides a unique way to document behaviour by cows on a continuous basis. Over 2 years 17 beef cows with calves were fitted with global positioning system (GPS) devices programmed to record uncorrected GPS locations at 1-s intervals in a semi-desert rangeland. Each cow was periodically observed during daylight hours and foraging, walking and stationary (standing/lying) activity times were recorded across days and individual cows to calculate a mean travel rate for each activity. Data without observers present were collected immediately preceding and following the abrupt weaning of calves at between 223 and 234 days of age to evaluate the potential of classifying various travel rates into foraging, walking and stationary activity. The three activities were further characterised within a 24-h period based on the sun's angle with respect to the horizon. Only data from cows whose equipment acquired !90% of the potential GPS positional data among consecutive days were analysed. Due to problems with the equipment, data from two cows in 2009 and two cows in 2011 met these criteria. The interval evaluated consisted of four 24-h periods before abrupt weaning and seven 24-h periods following weaning. Results suggested that uncorrected 1-s positional GPS data are satisfactory to classify the behaviour by free-ranging beef cows into foraging, walking and stationary activities. Furthermore, abrupt weaning caused cows to change their spatial and temporal behaviour across and within days. Overall, travel by cows increased post-weaning with subtle within-day behavioural changes. Further research will be required to fully understand the biological importance of spatio-temporal behaviour to optimise cattle and landscape management goals.
ABSTRACT Background/Question/Methods Standardized monitoring data collection efforts using a prob... more ABSTRACT Background/Question/Methods Standardized monitoring data collection efforts using a probabilistic sample design, such as in the Bureau of Land Management’s (BLM) Assessment, Inventory, and Monitoring (AIM) Strategy, provide a core suite of ecological indicators, maximize data collection efficiency, and promote reuse of monitoring datasets to address multiple ecosystem concerns at multiple scales. While AIM data have been collected in many locations, the process and potential insights of upscaling and/or downscaling monitoring data has not yet been demonstrated with these data. Our objective was to apply multi-scale monitoring data to answer questions related to overall landscape health, post-fire grazing closures, and emergency stabilization and rehabilitation (ES&R) treatment effectiveness in a 2.7 million acre study area in northern California and northwestern Nevada. To meet this multi-scale objective, we deployed the AIM strategy at three spatial scales and intensities: low-intensity landscape sampling across the study area, medium-intensity sampling of the nine livestock grazing allotments which intersected the Rush Fire of 2012 (610,345 acres), and high-intensity sampling of ES&R treatments within the fire boundary (2,558 acres). Terrestrial plots were established within a probabilistic sampling framework, and the BLM core indicators were measured at each sample point in order to meet our objectives. Sample weights were adjusted for each scale of analysis. Results/Conclusions We collected data at a total of 206 sample plots in 2013. At the landscape scale, indicator estimates varied with ecological potential, disturbance history, and land use. The sample points were then subset to answer management questions at local scales. ES&R treatment effectiveness plots (n=28) were found to have high bare ground, low foliar cover, and low perennial plant density. When we applied the data (n=106) to answer post-fire livestock grazing monitoring questions, low perennial grass cover and high bare ground indicate insufficient recovery 1 year after the fire to support extensive livestock grazing. However, the sample plot distribution was insufficient to answer management questions for all allotments studied at this scale. As we moved from fine-scale to broad-scale analysis, uncertainty increased. We anticipate additional years of sampling at additional locations will increase the power associated with the indicator estimates and decrease variance. Measuring indicators of ecosystem health describes ecosystem responses to pressures such as fire and grazing which can in turn guide land management decisions. This multi-scale monitoring approach maximizes efficiency in data collection, analysis, and reporting by acquiring the same ecological information at each scale and reusing data at each scale.
Electronic tracking provides a unique way to document behaviour by cows on a continuous basis. Ov... more Electronic tracking provides a unique way to document behaviour by cows on a continuous basis. Over 2 years 17 beef cows with calves were fitted with global positioning system (GPS) devices programmed to record uncorrected GPS locations at 1-s intervals in a semi-desert rangeland. Each cow was periodically observed during daylight hours and foraging, walking and stationary (standing/lying) activity times were recorded across days and individual cows to calculate a mean travel rate for each activity. Data without observers present were collected immediately preceding and following the abrupt weaning of calves at between 223 and 234 days of age to evaluate the potential of classifying various travel rates into foraging, walking and stationary activity. The three activities were further characterised within a 24-h period based on the sun's angle with respect to the horizon. Only data from cows whose equipment acquired !90% of the potential GPS positional data among consecutive days were analysed. Due to problems with the equipment, data from two cows in 2009 and two cows in 2011 met these criteria. The interval evaluated consisted of four 24-h periods before abrupt weaning and seven 24-h periods following weaning. Results suggested that uncorrected 1-s positional GPS data are satisfactory to classify the behaviour by free-ranging beef cows into foraging, walking and stationary activities. Furthermore, abrupt weaning caused cows to change their spatial and temporal behaviour across and within days. Overall, travel by cows increased post-weaning with subtle within-day behavioural changes. Further research will be required to fully understand the biological importance of spatio-temporal behaviour to optimise cattle and landscape management goals.
ABSTRACT Background/Question/Methods Standardized monitoring data collection efforts using a prob... more ABSTRACT Background/Question/Methods Standardized monitoring data collection efforts using a probabilistic sample design, such as in the Bureau of Land Management’s (BLM) Assessment, Inventory, and Monitoring (AIM) Strategy, provide a core suite of ecological indicators, maximize data collection efficiency, and promote reuse of monitoring datasets to address multiple ecosystem concerns at multiple scales. While AIM data have been collected in many locations, the process and potential insights of upscaling and/or downscaling monitoring data has not yet been demonstrated with these data. Our objective was to apply multi-scale monitoring data to answer questions related to overall landscape health, post-fire grazing closures, and emergency stabilization and rehabilitation (ES&R) treatment effectiveness in a 2.7 million acre study area in northern California and northwestern Nevada. To meet this multi-scale objective, we deployed the AIM strategy at three spatial scales and intensities: low-intensity landscape sampling across the study area, medium-intensity sampling of the nine livestock grazing allotments which intersected the Rush Fire of 2012 (610,345 acres), and high-intensity sampling of ES&R treatments within the fire boundary (2,558 acres). Terrestrial plots were established within a probabilistic sampling framework, and the BLM core indicators were measured at each sample point in order to meet our objectives. Sample weights were adjusted for each scale of analysis. Results/Conclusions We collected data at a total of 206 sample plots in 2013. At the landscape scale, indicator estimates varied with ecological potential, disturbance history, and land use. The sample points were then subset to answer management questions at local scales. ES&R treatment effectiveness plots (n=28) were found to have high bare ground, low foliar cover, and low perennial plant density. When we applied the data (n=106) to answer post-fire livestock grazing monitoring questions, low perennial grass cover and high bare ground indicate insufficient recovery 1 year after the fire to support extensive livestock grazing. However, the sample plot distribution was insufficient to answer management questions for all allotments studied at this scale. As we moved from fine-scale to broad-scale analysis, uncertainty increased. We anticipate additional years of sampling at additional locations will increase the power associated with the indicator estimates and decrease variance. Measuring indicators of ecosystem health describes ecosystem responses to pressures such as fire and grazing which can in turn guide land management decisions. This multi-scale monitoring approach maximizes efficiency in data collection, analysis, and reporting by acquiring the same ecological information at each scale and reusing data at each scale.
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