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  1. Abstract Background Global increases in human activity threaten connectivity of animal habitat and populations. Protection and restoration of wildlife habitat and movement corridors require robust models to forecast the effects of human activity on movement behaviour, resource selection, and connectivity. Recent research suggests that animal resource selection and responses to human activity depend on their behavioural movement state, with increased tolerance for human activity in fast states of movement. Yet, few studies have incorporated state-dependent movement behaviour into analyses of Merriam connectivity, that is individual-based metrics of connectivity that incorporate landscape structure and movement behaviour. Methods We assessed the cumulative effects of anthropogenic development on multiple movement processes including movement behaviour, resource selection, and Merriam connectivity. We simulated movement paths using hidden Markov movement models and step selection functions to estimate habitat use and connectivity for three landscape scenarios: reference conditions with no anthropogenic development, current conditions, and future conditions with a simulated expansion of towns and recreational trails. Our analysis used 20 years of grizzly bear ( Ursus arctos ) and gray wolf ( Canis lupus ) movement data collected in and around Banff National Park, Canada. Results Carnivores increased their speed of travel near towns and areas of highmore »trail and road density, presumably to avoid encounters with people. They exhibited stronger avoidance of anthropogenic development when foraging and resting compared to travelling and during the day compared to night. Wolves exhibited stronger avoidance of anthropogenic development than grizzly bears. Current development reduced the amount of high-quality habitat between two mountain towns by more than 35%. Habitat degradation constrained movement routes around towns and was most pronounced for foraging and resting behaviour. Current anthropogenic development reduced connectivity from reference conditions an average of 85%. Habitat quality and connectivity further declined under a future development scenario. Conclusions Our results highlight the cumulative effects of anthropogenic development on carnivore movement behaviour, habitat use, and connectivity. Our strong behaviour-specific responses to human activity suggest that conservation initiatives should consider how proposed developments and restoration actions would affect where animals travel and how they use the landscape.« less
    Free, publicly-accessible full text available December 1, 2023
  2. Abstract Surveillance of animal movements using electronic tags (i.e., biotelemetry) has emerged as an essential tool for both basic and applied ecological research and monitoring. Advances in animal tracking are occurring simultaneously with changes to technology, in an evolving global scientific culture that increasingly promotes data sharing and transparency. However, there is a risk that misuse of biotelemetry data could increase the vulnerability of animals to human disturbance or exploitation. For the most part, telemetry data security is not a danger to animals or their ecosystems, but for some high-risk cases, as with species’ with high economic value or at-risk populations, available knowledge of their movements may promote active disturbance or worse, potential poaching. We suggest that when designing animal tracking studies it is incumbent on scientists to consider the vulnerability of their study animals to risks arising from the implementation of the proposed program, and to take preventative measures.
  3. Abstract

    Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set ofGPSlocations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators exceptAKDEassume independent and identically distributed (IID) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation () to quantify the information content of each data set. We found thatAKDE95% areamore »estimates were larger than conventionalIID‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets byAKDE95% (or 50%) estimates was 95.3% (or 50.1%), confirming the largerAKDEranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing . To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated thatAKDEwas generally more accurate than conventional methods, particularly for small . While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an >1,000, where 30% had an <30. In this frequently encountered scenario of small ,AKDEwas the only estimator capable of producing an accurate home range estimate on autocorrelated data.

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