Home-range estimation within complex restricted environments: Importance of method selection in detecting seasonal change
Authors: Knight, C.M., Kenward, R.E., Gozlan, R.E., Hodder, K.H., Walls, S.S. and Lucas, M.C.
Journal: Wildlife Research
Volume: 36
Issue: 3
Pages: 213-224
ISSN: 1035-3712
DOI: 10.1071/WR08032
Abstract:Estimating the home ranges of animals from telemetry data can provide vital information on their spatial behaviour, which can be applied by managers to a wide range of situations including reserve design, habitat management and interactions between native and non-native species. Methods used to estimate home ranges of animals in spatially restricted environments (e.g. rivers) are liable to overestimate areas and underestimate travel distances by including unusable habitat (e.g. river bank). Currently, few studies that collect telemetry data from species in restricted environments maximise the information that can be gathered by using the most appropriate home-range estimation techniques. Simulated location datasets as well as radio-fix data from 23 northern pike (Esox lucius) were used to examine the efficiency of home-range and travel estimators, with and without correction for unusable habitat, for detecting seasonal changes in movements. Cluster analysis most clearly demonstrated changes in range area between seasons for empirical data, also showing changes in patchiness, and was least affected by unusable-environment error. Kernel analysis showed seasonal variation in range area more clearly than peripheral polygons or ellipses. Range span, a linear estimator of home range, had no significant seasonal variation. Results from all range area estimators were smallest in autumn, when cores were least fragmented and interlocation movements smallest. Cluster analysis showed that core ranges were largest and most fragmented in summer, when interlocation distances were most variable, whereas excursion-sensitive methods (e.g. kernels) recorded the largest outlines in spring, when interlocation distances were largest. Our results provide a rationale for a priori selection of home-range estimators in restricted environments. Contours containing 95% of the location density defined by kernel analyses better reflected excursive activity than ellipses or peripheral polygons, whereas cluster analyses better defined range cores in usable habitat and indicate range fragmentation. © 2009 CSIRO.
Source: Scopus
Home-range estimation within complex restricted environments: importance of method selection in detecting seasonal change
Authors: Knight, C.M., Kenward, R.E., Gozlan, R.E., Hodder, K.H., Walls, S.S. and Lucas, M.C.
Journal: WILDLIFE RESEARCH
Volume: 36
Issue: 3
Pages: 213-224
eISSN: 1448-5494
ISSN: 1035-3712
DOI: 10.1071/WR08032
Source: Web of Science (Lite)
Home Range Estimation Within a Complex Restricted Environment: Importance of Method Selection in Detecting Seasonal Change
Authors: Knight, C.M., Kenward, R.E., Gozlan, R.E., Hodder, K.H., Walls, S.S. and Lucas, M.C.
Journal: Wildlife research
Volume: 36
Pages: 213-224
ISSN: 1035-3712
DOI: 10.1071/WR08032
Abstract:1. Methods used to estimate home ranges in restricted environments such as rivers are currently based on standard analyses developed for unrestricted environments. These methods are limited in restricted environments, often overestimating home range area through the inclusion of habitats outside the restricted area.
2. Location data from 23 radio-tagged northern pike (Esox lucius) were used to examine the most appropriate method for investigating differences in home range estimation. The example used was seasonal home range area. Standard home range estimators were tested in addition to a new technique of clipping away the area of home range outside the river. Distances travelled by each individual were calculated using a standard technique and through the use of a midline to ensure the distance calculated followed the path of the river.
3. Pike home ranges and distances travelled were found to vary seasonally. Increased activity for spawning in spring increased home range area and reduced activity in autumn lead to a reduced home range. However, the method used to estimate the home range did influence the results.
4. Cluster analysis, minimum convex polygons (MCPs) and kernels were found to be robust range estimators, each favouring the prediction different range structures such as core area or excursive activity. 5. Clipping reduced out-of-bank error and hence bias generated by this error, aiding the clarity of the results. While use of a midline increased variability this was biological variation that had been masked by the previous method as it did not follow the shape of the river. Thus, excursive movements had been underestimated.
6. Our results show that no single home range estimator always produces the strongest results. Selection of the most appropriate estimator depends on the particular biological question being posed. Clipping and use of a midline can be used to adapt standard methods to better suit restricted environments.
Source: Manual
Preferred by: Kathy Hodder