Journal of Animal Ecology 2016, 85, 516–524
doi: 10.1111/1365-2656.12485
Incorporating animal spatial memory in step selection functions Luiz Gustavo R. Oliveira-Santos1*, James D. Forester2, Ubiratan Piovezan3, Walfrido M. Tomas3 and Fernando A. S. Fernandez1 1
Department of Ecology, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 68020, Brazil; 2Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, Saint Paul, MN 55108, USA; and 3Department of Wildlife, EMBRAPA PANTANAL – Brazilian Agricultural Research Corporation, Corumba, MS 70770-901, Brazil
Summary 1. Memory is among the most important and neglected forces that shapes animal movement patterns. Research on the movement–memory interface is crucial to understand how animals use spatial learning to navigate across space because memory-based navigation is directly linked to animals’ space use and home range behaviour; however, because memory cannot be measured directly, it is difficult to account for. 2. Here, we incorporated spatial memory into step selection functions (SSF) to understand how resource selection and spatial memory affect space use of feral hogs (Sus scrofa). We used Biased Random Bridge kernel estimates linked to residence time as a surrogate for memory and tested four conceptually different dynamic maps of spatial memory. We applied this memory-based SSF to a data set of hog relocations to evaluate the importance of land cover type, time of day and spatial memory on the animals’ space use. 3. Our approach has shown how the incorporation of spatial memory into animal movement models can improve estimates of habitat selection. Memory-based SSF provided a feasible way to gain insight into how animals use spatial learning to guide their movement decisions. 4. We found that while hogs selected forested areas and water bodies and avoided grasslands during the day (primarily at noon), they had a strong tendency to select previously visited areas, mainly those held in recent memory. Beyond actively updating their memory with recent experiences, hogs were able to discriminate among spatial memories encoded at different circadian phases of their activity. Even though hogs are thought to have long memory retention, they likely relied on recent experiences because the local food resources are quickly depleted and slowly renewed, yielding an uncertain spatial distribution of resources. Key-words: animal movement, Biased Random Bridge kernel estimation, cognitive maps, GPS tracking, habitat selection, spatial memory, Sus scrofa
Introduction Models that integrate biotic and abiotic environmental variables with internal animal states (Mandel et al. 2008), as well as behavioural modes (sensu Fryxell et al. 2008) and social contexts (Haydon et al. 2008), have yielded important insights into animal movement ecology. Despite meaningful advances, however, these approaches have largely avoided accounting for the cognitive capacities of individual animals, even though many species are thought to have the capacity for spatial learning (but see Dalziel, Morales & Fryxell 2008; Van Moorter et al. 2009). *Correspondence author. E-mail:
[email protected]
Navigation capacity in two- and three-dimensional space is critical to individual fitness because this ability permits animals to move rapidly, safely and efficiently across the landscape (Stamps 1995). Evolutionary forces have likely pushed animals to valorize familiar areas (Stamps 1995; Dukas 2004), because most animal species do not move randomly, but instead develop home ranges and core areas (Powell 2000; B€ orger, Dalziel & Fryxell 2008). Individuals moving within familiar areas can increase their foraging efficiency (Henry & Stoner 2011; Tsoar et al. 2011), escape success from predators (Ambrose 1972) and access to mates (Dukas 2004). Memory is a crucial component of animal cognition that affects how animals move across and recognize important
© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society
Incorporating animal memory in SSF features in the landscapes where they live (Dukas 2004; B€ orger, Dalziel & Fryxell 2008; Fagan et al. 2013). Individuals rely on their spatial memory or cognitive maps to survive (sensu Gallistel 1994; but see Bennett 1996). Remembered landmarks are key cues to allow non-random navigation, and such cues range from simple visual references and scent marks, to sophisticated use of infrasound, polarized and ultraviolet light, magnetic fields, air and water pressure, and sun or other stars for orientation (Pearce 2008). Species with little or no capacity for flight may use path integration during the navigation and/or look for memorized visual landmarks along their route (Bennett 1996). While walking, individuals access both their reference memory (long-term memory associated to events far in the past) and working memory (short-term memory associated with recent experiences) to make navigation decisions (Baddeley 1992); the value of new spatial information is interchangeable between these two memories (Howery, Bailey & Laca 1999). Thus, relevant new information could be stored in the reference memory (possibly reinforcing existing memory), but older, less reinforced information would decay through time (i.e. memory updating). Resource selection is an important component of movement decisions because individuals choose resource patches based on what they need, what is available and how difficult each resource is to access at a given location and time (Fortin et al. 2005; Forester, Im & Rathouz 2009). This selection process is not memory free, and the memory capacity of an animal, along with its past experience, will influence both the way in which the animal navigates and the habitat choices it makes (Dalziel, Morales & Fryxell 2008; Avgar, Deardon & Fryxell 2013; Fagan et al. 2013). Although spatial memory is acknowledged as relevant in the resource selection process, the hidden and dynamic nature of this cognitive trait hinders the measurement of its importance in free-ranging animals. If past experiences can be reinforced or decay in memory and also affect future movements, an additional challenge is to define over what time-scale encoded and stored information is used. We lack understanding about how spatial memory affects movement decisions because we have little knowledge regarding the relative importance of long- and shortterm memory for animals in natural conditions. Here, we investigated how feral hogs (Sus scrofa Linnaeus 1758), a social, cooperative, long-lived species, use spatial memory during resource selection in a heterogeneous landscape. As hogs have been shown to have good cognitive skills and memory retention (Gieling, Elizabeth & van der Staay 2011), we expected that they would preferentially use long-term memory to inform habitat selection decisions, while recent experiences should be less important. Furthermore, we hypothesized that hogs could discriminate information memorized at different circadian phases of their activity (day and night) and recall them in
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the future at the same phase. To test these hypotheses, we developed a model that incorporated spatial memory into step selection functions (SSF; Fortin et al. 2005; Forester, Im & Rathouz 2009). We built and tested four conceptually different proxies for dynamic spatial memories – all based on Biased Random Bridge kernel estimates based on residence time (Benhamou & Riotte-Lambert 2012) – and included them within SSFs. These proxies of spatial memory were built to accommodate our hypotheses regarding the importance of long- and short-term experiences, and whether individuals are able to discriminate between information stored during the day and night. This approach permits us to evaluate contrasting alternate hypotheses about how spatial memory operates in the habitat selection process, as well as to understand how resource selection changes as a function of the time of day.
Materials and methods study area and habitat classification The study was carried out in the Pantanal wetland, Brazil (between 18°510 S and 19°100 S; and between 56°300 W and 56°540 W). The main vegetation types found in the landscape are seasonal floodable and non-floodable grasslands, forest patches and temporary freshwater or alkaline lakes. The average annual temperature is 26 °C, and average annual rainfall is 1100 mm, but 80% of the annual rainfall occurs from December to May. We used LANDSAT TM satellite images recorded between July 2010 and April 2012 to classify the area into four habitat types that are biologically relevant to hog ecology: dry grasslands, wet grasslands, water bodies and forest patches. See Appendix S1 (Supporting information) for details on the satellite image processing and classification, as well as the adequacy of different maps for each individual.
studied feral hog population Feral hogs were introduced in the Pantanal wetland about 300 year ago. Currently, they represent the highest mammal biomass and are the most hunted species within this ecosystem (Desbiez, Bodmer & Tomas 2010; Desbiez et al. 2011). Usually, females live in herds (mean = 8, range = 2–40 individuals) composed of adult females, juveniles and subadults of both sexes, while males are solitary (except during mating season) (Desbiez, Bodmer & Tomas 2010; Oliveira-Santos et al. 2011). They are mainly nocturnal (Oliveira-Santos, Zucco & Agostinelli 2013), have generalist food habits (leaves, roots, fruits and invertebrates) and inhabit all type of habitats (wet and dry grasslands, forest areas and savannas) (Desbiez et al. 2009).
capture and tracking Captures were carried out during 10 days in July 2011 and 10 days in March 2012. Hogs were captured (one animal per herd) by active searching via truck or horse, using trained dogs and local cowboy assistants. Once captured, hogs were anesthetized, sexed, weighed, aged and fitted with GPS collars. The
© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology, 85, 516–524
518 L. G. R. Oliveira-Santos et al. reproductive condition of all females (pregnant, lactating or nonreproductive) was verified before release. We built GPS collars by modifying a commercial GPS unit used by runners; this unit was attached to a VHF transmitter and scheduled to record locations every 5 min. Because the GPS error (static test error; mean = 20 m, SD = 3 m) was usually larger than the individual displacement within 5 min and the resource selection of landscape cover types is likely to occur over longer time-scales, we subsampled the data to hourly time intervals. The movement between two temporally adjacent locations (i.e. the starting and ending points of a sample interval) was defined as a ‘step’. Each step’s bearing and turning angle (the latter defined as the change in bearing between two adjacent steps) were also calculated. The animals were located at least 5–10 times a month by homing in on the signal from the VHF beacon, and recaptures to recover the collars were carried out in the same way as the captures. See Appendix S2 for details on capture and recapture procedures, animal handling, anaesthesia protocol and GPS collar construction.
dynamic cognitive maps Because the time spent in a given place is positively correlated with both spatial learning and memory life span (Pearce 2008), we used the Biased Random Bridge kernel estimator (BRB; Benhamou 2011) based on residence time as a surrogate for spatial memory (Benhamou & Riotte-Lambert 2012). The BRB is based on the properties of a biased random walk between successive pairs of locations, assuming dependency on the time between locations and using Brownian motion variance to describe the animal’s mobility. The theory of Brownian motion, based on a continuous-time stochastic model, describes the random trajectory of particles suspended in a fluid (e.g. liquid or gas). Animal movement can also be represented as the continuous trajectory of a particle (i.e. an individual animal) as it moves across space and through time (Turchin 1998), but we are usually unable to follow this trajectory continuously. Commonly, we have a set of discrete relocations, with some level of autocorrelation, taken from real trajectories; these data are typically collected by GPS collars or via VHF telemetry. Despite having a certain level of confidence (GPS or triangulation error) about each recorded location, we do not know the path the animal took between consecutive relocations; however, we can use a Brownian bridge (Bullard 1999) to describe the probability of space use by the animal over that time interval. A Brownian bridge depicts the Brownian motion (i.e. random walk) process conditioned by known starting and ending locations, the time interval between them and the animal’s expected rate of movement; this approach assumes a purely diffusive process, with increasing uncertainty as the distance from the known relocations increases (Bullard 1999; Horne et al. 2007). Here, we used a modified Brownian bridge approach, the Biased Random Bridge (Benhamou 2011; Benhamou & RiotteLambert 2012), that assumes an advection–diffusion process (i.e. a biased random walk) between locations. The probability density surfaces describing space use between consecutive locations (i.e. the BRBs) are summed and normalized to 1. This produces a 2dimensional utilization distribution of an animal’s trajectory and represents the relative time spent in different areas. By relying on the activity time between successive locations (movement-based) instead of the local density of individual
locations (a more static view), we can assess the time that an animal spent in each area, providing a more biologically relevant measure of intensity of use (in our case, the time of exposure to local information). The BRB based on residence time was formally called the intensity distribution (ID) by Benhamou & Riotte-Lambert (2012). The residence time associated with each relocation is computed as the sum of time durations that the animal spent during the whole trajectory within a circle (with constant radius) centred on each relocation. The ID estimator is convenient for spatial memory studies because it is movement based and considers the temporal link between successive relocations by estimating spatially explicit mean residence time per visit. To incorporate the effect of spatial memory on resource selection while maintaining a constant data set, we removed all individuals’ relocations that were collected during the first 3 days of tracking. However, we used these removed data to estimate the ID for each individual (hence initializing spatial memory). For each additional step, we dynamically incorporated the new relocations to re-estimate the ID (memory updating). This re-estimation was calculated in four ways, each representing a conceptually different spatial memory map: (i) Recent memory = for each step, we used only relocations from the previous 3 days to estimate spatial memory; (ii) Recent temporal memory = for each step, we estimated two spatial memory maps using only the last 3 days: one considering only diurnal relocations (06.00–18.00 h), and another considering only nocturnal relocations (18.01–05.59 h); (iii) Long-term memory = for each step, we used all previous relocations to estimate spatial memory; (iv) Long-term temporal memory = for each step, we estimated two spatial memory maps using all previous relocations split into diurnal and nocturnal relocations (see animation showing the dynamic nature of these four approaches to estimating spatial memory in Appendix S3). For analysis, each spatial memory map (i.e. a density estimate) was converted to a continuous isopleth value between 0 and 1: values around 0 identify the areas that are most strongly represented within an animal’s spatial memory (e.g. familiar areas or memory core areas), and values around 1 identify the least familiar areas. The spatial memory maps were created with the BRB function in the adehabitatHR package (Calenge 2006) of program R (R Core Team 2013). See Appendix S4 for details on the estimation of spatial memory.
habitat characterization of the used areas In order to describe the habitat used during GPS tracking, we calculated the proportion of each habitat type within the area used by each individual. The area used was estimated using BRB (Benhamou & Riotte-Lambert 2012) in four ways: (i) utilization density under the 95% isopleth (UD95%), (ii) utilization density under the 50% isopleth (UD50%), (iii) recursion distribution density under the 30% isopleth (RD30%) and (iv) intensity distribution density under the 30% isopleth (ID30%). The ID and RD are two biologically meaningful derivations of a UD, corresponding to the spatial distribution of the mean residence time per visit, and the spatial distribution of the number of visits, respectively. For each individual and type of BRB estimation (UD, ID and RD), we calculated the proportion of habitat types contained in the area. Furthermore, we calculated the proportion of habitat types surrounding the UD95% isopleth, buffering this estimated
© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology, 85, 516–524
Incorporating animal memory in SSF area using a radius of 7 km. See Appendix S5 for details on the area estimation and habitat proportion calculation in R.
incorporating memory into step selection functions We tested the effects of cover type, time of day, cover type and time of day interaction, and four conceptually different spatial memories (see Dynamic Cognitive Maps) on the resource selection of hogs using step selection functions (SSF; Fortin et al. 2005; Forester, Im & Rathouz 2009). The time of day was included in the SSF using harmonics to allow a nonlinear relationship between time of day and selection strength (sensu Forester, Im & Rathouz 2009, see Appendix S4). Therefore, we included four terms (c1, c2, s2 and s2) interacted with cover type for each step j from individual i: c1ij ¼ cos decimal hourij 2p=24 c2ij ¼ cos decimal hourij 4p=24 s1ij ¼ sin decimal hourij 2p=24 s2ij ¼ sin decimal hourij 4p=24 The SSF is an extension of the well-known resource selection function (RSF; ‘used-available’; Manly et al. 2002) that incorporates the movement process. The continuous movement trajectory of an animal is sampled at regular temporal intervals (in this case every hour) to create a series of step lengths and turning angles. To account for how resource availability changes across space, we generated 50 random steps originating from the starting location of each individual step. The random steps were generated based on independent random samples from the observed step length and turning angle distributions of each individual (modified from the approach taken by Fortin et al. 2005). This independent sampling was acceptable because there was no correlation between step length and turning angle (circular–linear correlation; |r| < 001, P > 045). The cover type at the end point of each observed and random step was recorded. Spatial memory was incorporated into the SSF by intersecting the endpoints of all steps with each of the four dynamic spatial memory maps (see animation showing the random step generation on a dynamic spatial memory map in Appendix S6). We used conditional logistic regression (CLR) to estimate the SSFs. This approach uses the standard exponential form to describe the probability of moving from location a to location b given the domain of availability, S and covariates Z: Prðcase ¼ bjS; a; ZÞ ¼ P
expðZðbÞ0 bÞ 0 l2S expðZðlÞ bÞ
where the b is a vector of coefficients describing the relative strength of selection for the covariates in Z. The CLR was conditioned to each step within individual (where observed steps were scored as ‘1’ and random steps were scored as ‘0’), and we calculated robust standard errors for the estimated CLR coefficients to take into account autocorrelation between successive steps within individuals (Forester, Im & Rathouz 2009). Forester, Im &
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Rathouz (2009) suggested including step length into SSFs to reduce the parameter bias; however, in this case, our parameter estimates were not sensitive to its inclusion. We fit seven SSF models where the linear predictor (i.e. Z(b)0 b) consisted of: 1 2 3 4 5 6 7
[Null Model]. Cover type [Habitat model]. Cover type + Cover type * Time of day [Habitat Time Model (HT)]. Cover type + Cover type * Time of day + Recent memory [HT-Recent memory]. Cover type + Cover type * Time of day + Recent Temporal memory [HT-Recent temporal memory]. Cover type + Cover type * Time of day + Long-term memory [HT-Long-term memory]. Cover type + Cover type * Time of day + Long-term Temporal memory [HT-Long-term temporal memory].
For all above models, the interaction term between cover type and time of day (Cover type * Time of day) was included as an interaction between Cover type and each of the four time harmonics (c1, c2, s1, s2). The fitted models were ranked following the Akaike Information Criterion (AIC; Burnham & Anderson 2002). To calculate the robust standard errors, we fit a linear mixed model to the residuals of the CLR with the individual identity as random effect plus a first-order autoregressive correlation structure. Our inspection of the autocorrelation plot of the linear mixed model suggested that autocorrelation for all individuals disappeared after a lag of 18 h. We refit the CLR with robust standard errors by using 18-step clusters for each individual. To examine individual variability in resource selection and to test the generality of our results, we fit the seven SSF models to data from each individual. The models for each individual were also ranked using AIC. See Appendix S7 for details on the memory-based SSF, CLR fitting, robust standard error estimation, as well as the R packages and functions.
Results We captured 26 hogs (12 males and 14 females) and tracked them from 45 to 233 days, yielding 726–17 167 5min steps for each individual. The landscape surrounding the areas used by the hogs was mainly composed of open areas (dry and wet grasslands ~64%), forest patches (~33%) and water bodies (~3%) (Fig. 1). However, their home range (UD95%) held consistently more wet grassland and water bodies and less dry grassland and forest patches, while the core areas (UD50%) presented less open and more forested area. The hogs spent more time per visit (ID30%) in open areas, but more frequently visited (RD30%) forested areas. The SSF analysis that included all individuals suggested that models taking spatial memory into account (memory-based SSF) performed much better than those based exclusively on habitat and time of day (DAIC > 859443). Among the conceptually different spatial memories tested, the recent temporal memory presented the highest support (model weight ~1; Fig. 2, Table S8.1). See Appendix S8 for the coefficient estimates and 95% confidence intervals
© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology, 85, 516–524
520 L. G. R. Oliveira-Santos et al.
Fig. 1. Habitat characteristics of the areas used by the tracked hogs. Studied area = areas surrounding the UD95% (radius 7 km). UD95% = Biased Random Bridge kernel estimation (BRB) using utilization distribution (UD) under probability isopleth of 95%. UD50% = BRB using UD under probability isopleths of 50%. ID30% = BRB using intensity distribution (ID) under isopleths of 30%. RD30% = BRB using recursion distribution (RD) under isopleths of 30%. Each dotted line depicts one individual; dots and bars represent mean 95% confidence intervals.
of all ranked models. The importance of spatial memory, mainly recent memory, was widespread among individuals (Table 1, Fig. 2), as recent temporal memory or recent memory models ranked the highest for all 26 individuals (17 and 9, respectively). Although the habitat type and time of day models (standard SSF) performed much worse than the ones with the additive effect of spatial memory (memory-based SSF), habitat and time of day had a strong contribution to the likelihood of the best models (Fig. 2). Step selection functions considering only the types of spatial memory had similar support to those considering only habitat and time of day; however, the standard SSF appeared to yield biased estimates (bias = coefficients estimated by standard SSF/coefficients
estimated by memory-based SSF), tending to overestimate the strength of resource selection (Fig. 3a). Based on the best-ranked model (Table S8.1; HTRecent temporal memory), hogs changed their resource selection through the day (Fig. 3b; see the interaction between time of day harmonics and habitat in Appendix S8). Using dry grasslands as reference habitat, forest patches and water bodies were always selected for, regardless of the time of day. However, selection for forest patches and water bodies was higher at noon and dusk, respectively. Selection for wet grasslands was nonsignificant at night (from 18.00 to 08.00 h) and negative at noon. Hogs consistently selected areas held in their recent memory (Fig. 3c) and avoided areas not visited
© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology, 85, 516–524
Incorporating animal memory in SSF
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Discussion
Fig. 2. Conditional logistic regression parameters estimated by the best-ranked (Table H.1) memory-based SSF (Model HTRecent temporal memory). Large grey dots depict the parameters estimated by the model that included all individuals. Small black dots depict the parameters estimated in models fit to each individual, and large black dots depict the parameter values averaged across the individual models.
Table 1. Competitive SSF models ranking among individuals. The values indicate the number of individuals by model ranking Ranking based on AIC Models Null Habitat Habitat time (HT) HT-Long-term memory HT-Long-term temporal memory HT-Recent memory HT-Recent temporal memory
1°
2°
3°
4°
5°
6°
7°
0 0 0 0 0
0 0 0 0 1
0 0 0 12 13
0 0 0 14 12
0 11 15 0 0
1 15 10 0 0
25 0 1 0 0
9 17
17 8
0 1
0 0
0 0
0 0
0 0
recently. When we permitted an interaction term between spatial memory (recent temporal memory) and time of day in the memory-based SSF, the expanded model had greater support than the one with only an additive memory effect (DAIC = 44; Table S8.8). According to this last model, the selection of areas held in recent temporal memory was higher during the day (mainly at noon) than at night and crepuscular hours (Fig. 3d). This difference of selection among periods of the day became sharper if the animal was moving far from the familiar areas.
Our results are consistent with the theoretical view that animal movement is influenced by a cognitive map and that this map is updated dynamically by adding new information to memory and discarding information that becomes old or useless (Powell 2000; Van Moorter et al. 2009; Avgar, Deardon & Fryxell 2013; Fagan et al. 2013). This approach opens new possibilities to integrate cognition traits (e.g. spatial memory, memory life span and learning capacity) with current methods of investigating animal movement. The memory-based SSF performed much better than the standard SSF (Fortin et al. 2005; Forester, Im & Rathouz 2009) for all individuals, irrespective of which conceptual spatial memory was used. Because most animal species do not move randomly across space, it would be naive to assume that individuals move only in response to exogenous factors. The field of movement ecology has been advanced by testing how internal states influence animal movement decisions (e.g. heart rate, body temperature, reproductive status; Nathan et al. 2008). Here, we highlight the need to incorporate internal states associated with learning and memory (Dalziel, Morales & Fryxell 2008; Van Moorter et al. 2009; Fagan et al. 2013). Our approach helps to reduce the entanglement between habitat effects and spatial memory effects when analysing movement decisions made by animals. Individuals had a strong tendency to return to previously visited areas (Riotte-Lambert, Benhamou & Chamaille-Jammes 2013); to ignore this, memory capacity could lead to hugely biased estimates of resource selection coefficients. It is likely that spatial memory interacts with external environmental variables and internal physiological states (Dalziel, Morales & Fryxell 2008), and not only in a simple and additive way as modelled here. For instance, the informational value of spatial memory could change in relation to time of day, habitat type, physiological state or behavioural interest. There could also be a trade-off between the relative importance of recent vs. long-term memory according to different ecosystems and different cognitive capacities of species. Biological memory cannot be measured directly – it is a latent state variable on which we attempt to draw inference by observing patterns in animal behaviour. Our approach to infer spatial memory relies on the assumption that previous time spent in an area contributes to the intensity of that location in an animal’s memory map. Clearly, this statistical description of memory is only a rough approximation of the true process and could be confounded by selection for resources under some conditions. However, we feel that our model selection approach reduces this risk. If the spatial distribution of resources was the only pattern driving space use, adding memory to the model would not appreciably increase fit. One exception to this could be if an unmeasured, but important, covariate co-occurred with some patches of selected
© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology, 85, 516–524
522 L. G. R. Oliveira-Santos et al. (a)
(b)
(c)
(d)
Fig. 3. (a) Bias in the estimates of habitat selection strength of traditional SSF in relation to memory-based SSF (HT-Recent temporal memory model, see Table H.1). Each dot depicts the bias of the estimates in models for individual hogs. (b) Relative habitat selection strength by feral hogs through the day. (c) Relative selection strength for recent temporal memory density based on the best-ranked model (HT-Recent temporal memory model). The dotted lines around all estimates depict the point-wise 95% confidence interval. (d) Relative selection strength for areas held in recent temporal memory (low values = higher memory), when an interaction term – between density of the spatial recent temporal memory and time of day – was added to the model (white is high avoidance of unfamiliar areas, dark is low avoidance).
resources but not others; even if animals were followed for a long period of time, this could give the appearance of a recent memory effect. The fact that we observed animals avoiding low-memory areas during the day (when high-memory areas mostly overlapped patches of forest and water) and selecting for them at night (when highmemory areas overlapped with the grasslands preferred for foraging) suggests that we are not having that problem here. Grasslands dominate the Pantanal landscape, and this cover type was the most used by hogs. However, most locations were concentrated within or near a few forest patches and water bodies. Forested areas corresponded
mainly to resting sites. Hogs returned to them (higher fidelity) more often than to grasslands, which represented the main feeding sites visited for longer times, but with a lower fidelity. Hogs selected forested areas and water bodies throughout the day and avoided wet grasslands during daylight hours. Habitat selection of large herbivores is strongly governed by the trade-offs among predation risk, thermal comfort and forage quality (Fortin et al. 2005; Forester, Im & Rathouz 2009; van Beest, Van Moorter & Milner 2012). We believe these three drivers could influence hog behaviour. Diurnal hunting by human hunters is the main source of hog mortality in the area, and the search for
© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology, 85, 516–524
Incorporating animal memory in SSF hogs is carried out primarily in open areas (Desbiez et al. 2011). Furthermore, hogs lack sweat glands, have a low performance kidney and depend on behavioural strategies (e.g. resting or wallowing in water or mud) to maintain their thermal and water balance (Sowls 1997; Zervanos 2002). Their diet is mainly composed of grass, roots, invertebrates and aquatic plants found in grasslands or around water bodies, but it also includes palm fruits found in forested areas (Desbiez et al. 2009). Hogs are usually crepuscular–nocturnal in Pantanal (Oliveira-Santos, Zucco & Agostinelli 2013), and the avoidance of open areas during the day can decrease their exposure to both hunting and high temperatures. The hunting and thermal protection provided by forested and aquatic habitats is most important during the hotter hours of the day, when we observed increased selection for forest and water bodies. At night, when both hunting and thermal exposure decrease, hogs increase their selection of grasslands for foraging. The strong support for the recent temporal memory model suggests that hogs were able to spatially discriminate areas important for diurnal and nocturnal activities. Pigs are a highly cooperative and cognitive species, have a large well-developed brain and have the ability to discriminate among objects and develop spatial learning (see review by Gieling, Elizabeth & van der Staay 2011). Given these cognitive skills, it was initially surprising that recent memory was more important to the movement process than long-term memory; however, different cover types are patchily distributed in the Pantanal wetland, and the feeding behaviour of hogs strongly alters habitat structure, especially when they forage in large herds (Sowls 1997). In this context, a good foraging area could quickly become overused, and then, the information held in the recent memory could be more relevant even if the species has high-memory durability. The decay rate of memory should depend on the species-specific cognitive abilities (Avgar, Deardon & Fryxell 2013) in addition to foraging mode and the renewal capacity and stability of the ecosystems (Fagan et al. 2013). To our knowledge, Dalziel, Morales & Fryxell (2008) were the first researchers who attempted to incorporate dynamic spatial memory effects into habitat selection models. Similar to our results, they found that elk (Cervus canadensis) movements were mostly governed by memory; however, in the case of the elk, habitat structure had little importance. These results show that spatial memory is potentially an important and neglected component of animal movement. In our case, habitat and time of day were important variables, and the strong support for models that allowed an interaction term between memory and time of day suggested that the relevance of memory varies through the day. In general, hogs tended to avoid areas they did not hold in their memory, but this avoidance was stronger around noon. It is reasonable to believe the animals rely more strongly on memory during the resting hours, looking for safety in very well-known places, while
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the exploratory displacements (those that occur beyond the spatial extent of recorded memory) are rare and should occur during the moments of high activity and attention. Overall, the BRB approach is easy to apply and provides a useful proxy for the complex and hidden process of animal memory. The incorporation of spatial memory into the standard SSF greatly improved the models and permitted us to disentangle the effects of habitat from those of spatial memory. Our approach allowed us to understand the relevance of memory for movement decisions, and this flexible model formulation will be useful in further research focused on how environmental and internal states interact with the memory. We show, for a highly cognitive species, that spatial memory use for movement decisions is a dynamic process in which free-ranging individuals can actively update their memory with recent experiences. Furthermore, spatial memories stored and encoded at different circadian activity phases are recalled later at the same phase. Finally, even species with long memory retention must rely mainly on recent experience if the resources are quickly depleted, are slowly renewed or are otherwise presented in an uncertain spatio-temporal distribution.
Acknowledgements This manuscript was greatly improved by comments from three anonymous reviewers. LGROS and FASF are supported by CNPq, and JDF received support from the UMN Institute on the Environment. We thank EMBRAPA Pantanal and FAPERJ (APQ1-2009) for logistical and financial support. We also thank cowboys Marcio Silva and Oziel A. Silva for their valuable help and for sharing their experience during the hog capture. The permission for hog capture was conceded by the Brazilian Environment Agency (Permit number: ICBIO 21560-1).
Data accessibility The raw data of the hogs’ movement and the R code to run the memory-based step selection function are available from the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.s5812 (Oliveira-Santos et al. 2016).
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Supporting Information Additional Supporting Information may be found in the online version of this article. Appendix S1. Details on the satellite image processing and classification, as well as the adequacy of different maps for each individual. Appendix S2. Details on capture and recapture procedures, animal handling, anesthesia protocol and GPS-collar construction. Appendix S3. Animation showing the dynamic nature of the four approaches to estimating spatial memory. Appendix S4. Details on the estimation of spatial memory.
Appendix S5. Details on the area estimation and habitat proportion calculation in R. Appendix S6. Animation showing the random step generation on a dynamic spatial memory map. Appendix S7. Details on the memory-based SSF, CLR fitting, robust standard error estimation, as well as the R packages and functions. Appendix S8. Coefficient estimates and 95% confidence intervals of all ranked models.
© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society, Journal of Animal Ecology, 85, 516–524
