Skip to main content
Fig. 4 | Ecological Processes

Fig. 4

From: A hierarchical path-segmentation movement ecology framework

Fig. 4

Current scale-dependent analytical methods for analyzing movement paths. The temporal range is only suggestive and best applied to medium and large vertebrates. Also the image placements are not precise and some methods, such as machine learning (Thessen 2016), can be applied to data at any scale, but here are associated with the scale at which they are likely to be most useful. In addition, deep learning (useful for identifying different types of long-term patterns) is actually a subset of machine learning (where other machine techniques, such as random forests and support vector machines have been applied to accelerometer data; Nathan et al. 2012; Fehlmann et al. 2017). Stochastic walks include correlated and biased random walks (Johnson et al. 2008). Space–time residence analyses represent a family of methods that include first-passage-time (FPT; Fauchald and Tveraa 2003; McKenzie et al. 2009) and related approaches (Torres et al. 2017), while recurrence analyses cover a plethora of methods used to identify recursive movement patterns (Berger-Tal and Bar-David 2015; Bar-David et al. 2009). The melange of images is extracted from publications in the literature (Nathan et al. 2012; Panzacchi et al. 2016; Morales et al. 2004; DeCesare et al. 2012; Wittemyer et al. 2008; Lyons et al. 2013; Abrahms et al. 2017; Fleming and Calabrese 2017; Pohle et al. 2017; Torres et al. 2017; Gurarie et al. 2017), as well as created for this publication

Back to article page