1.3. Home range occupancy and dispersal potential
1.3.1. Explicitly modelling home range occupancy and dispersal potential with IBMs
We can define dispersal as fine scale movements of foraging, mating, or avoiding predators translated to larger scales, such as landscapes, linking these behaviours to habitat selection, range expansion, regional dispersal, and species distribution (Fig 1, section 2). Therefore, dispersal ability is useful for estimating how populations (Fahrig 2003; Latombe et al. 2014; Aben et al. 2014) and communities (Buchmann et al. 2011) respond to fragmented habitats; however, assessing dispersal ability and range expansion often involves species responses to resource availability and interacting individuals, such as avoiding predators, curbing mortality risk, and group social information, using metrics to assess habitat suitability (Kramer-Schadt et al. 2004; Kramer-Schadt et al. 2005; Bocedi, Palmer, et al. 2014; Aben et al. 2014; Mooij & DeAngelis 2003; Bocedi, Zurell, et al. 2014; Hayes & Thompson 2013; Henry et al. 2014). Therefore, habitat suitability indices to predict animal occupancy and range dynamics may ignore important behavioural detail, thereby diluting subtle, but decisive, movement mechanisms (Schurr et al. 2012; Kubisch et al. 2014; Mooij & DeAngelis 2003). For example, dispersal is a by-product of recurring, frequent movement linked to common behaviour connecting animals to space, such as resource use (van Dyck & Baguette 2005), competition (Moorcroft et al. 2006), mating opportunities (Wang & Grimm 2007), and mortality risk (Kramer-Schadt et al. 2005; Bocedi, Zurell, et al. 2014; Kramer-Schadt et al. 2004), in turn forming home ranges. Home range size and dispersal can then vary under combinations of these constraints, which influences future dispersal potential of individuals and thus population structure (Wang & Grimm 2007; Bocedi, Palmer, et al. 2014) over different time and space scales (Bocedi, Zurell, et al. 2014; Henry et al. 2014; Bowler & Benton 2005; Kramer-Schadt et al. 2011; Kanagaraj et al. 2013).
Home range behaviour is often complemented by individual traits such as cognition and thus directly related to memory, as animals occupy home ranges by revisiting previous sites. Animals can stabilise their home ranges by relying on memory over exploring new resources (Nabe-Nielsen et al. 2013) and using complementary types of memory e.g. reference and working (Van Moorter et al. 2009). However, because home range stability depends on scale (Lyons et al. 2013), home range models must balance the cost of animals requiring too much information (see (Börger et al. 2008)). Other key movement traits linked to home range behaviour, such as duration and frequency of patch visits, may include mechanisms that support biologically relevant movement rules and share direct trade-offs with the environment (Lyons et al. 2013; Van Moorter et al. 2015). Models that capture individual responses to resource, competitor, predator, or mate distribution, for example, are useful at predicting home range behaviour because they define the spatial limits of animal movement (Mitchell & Powell 2004; Moorcroft et al. 2006; Buchmann et al. 2011; Lyons et al. 2013; Bocedi, Zurell, et al. 2014; Bocedi, Palmer, et al. 2014). Therefore, future avenues where movement IBMs are well suited involve scenarios where home ranges are defined by individual preferences and how they change across different environments (Börger et al. 2008).
1.3.2. General individual traits link resource use to home range behaviour in space and time
Home range IBMs linked to energetics generally use resource intake as a proxy, i.e. resource sites visited (Mitchell & Powell 2004; Nabe-Nielsen et al. 2013) or patches consumed (Van Moorter et al. 2009; Buchmann et al. 2011), rather than individual energy reserves, highlighting a disparity in how we interpret individual-based energetics with home range models. These works propose that direct links animals share with their environment help animals form home ranges: selecting resource sites rather than simply movement to define home ranges (Mitchell & Powell 2004), using attraction rules to nearby, previously visited food (Nabe-Nielsen et al. 2013) or shelter (Moorcroft et al. 2006) to generate more stable home ranges. Because energy intake depends on patch residence time, visiting fewer patches but for longer can stabilise home range size (Nabe-Nielsen et al. 2013) depending on food renewal rates (Van Moorter et al. 2009). Home range models assuming animals optimise resource use based on their dispersal in the landscape can accurately predict real home range size in black bears (Mitchell & Powell 2007) when animals use less area and fewer resources (Mitchell & Powell 2012), despite not strictly including individual energetics. Further, foraging strategy can shift with body mass, which can generate different home range sizes based on efficiency of resource use (Buchmann et al. 2012; Buchmann et al. 2011). Therefore, behavioural strategy can determine the potential movement costs required to optimise space use and generate stable home ranges. Mechanistic home range models based on movement rules (Van Moorter et al. 2009) could be more general and realistic by using mechanisms like energetics, rather than relying on random walks to drive movement in space.
1.3.3. Energetics IBMs are useful frameworks for modelling complex home range behaviour
A common, but abstract way to integrate energetics into movement is depicting decision-making surfaces based on resource availability or mortality risk (e.g. (Fronhofer et al. 2013; Pauli et al. 2013), which can translate small-scale movement models to the larger home range and regional scale by unifying energy uptake and use from the individual level (Fig. 1). Supplementing energetics with other traits like memory can make movement models more predictive and realistic (Latombe et al. 2014). For example, the restricted movement of individuals by energy costs may override the strength of reference memory, producing less sustainable home ranges. Energy intake changes in space; irrelevant patch use by animals visiting empty patches in a landscape can shape how we think animals forge home ranges (Mitchell & Powell 2012). This highlights the intimacy energetics share with other traits that models ought to balance depending on the species or system modelled. Home range behaviour and dispersal involve multiple cues (Pe’er & Kramer-Schadt 2008; Schick et al. 2008), so models assuming animals ignore time for other important activities in addition to finding resources (Mitchell & Powell 2004) can be misleading and inaccurate. Energy use that animals abide by across time and space thus represents useful logic when reasoning what general mechanism could usefully summarise complex movement, such as home range behaviour. This complexity teaches us exploring home range behaviour using mechanisms is simplified when movement decisions stem from cues directly reflecting the processes shaping home range behaviour, such as when internal energy reserves influence animals to compromise or exploit resources or mates in a landscape.