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Article

Goal-Directed Travel in the Nocturnal Javan Slow Loris (Nycticebus javanicus)

by
Stephanie A. Poindexter
1,2,*,
Vincent Nijman
2,
Muhammed Ali Imron
3 and
K. Anne-Isola Nekaris
2,3,*
1
Anthropology Department, University at Buffalo, Buffalo, NY 14260, USA
2
Nocturnal Primate Research Group, School of Social Sciences, Oxford Brookes University, Oxford OX3 0BP, UK
3
Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
*
Authors to whom correspondence should be addressed.
Ecologies 2023, 4(3), 568-579; https://doi.org/10.3390/ecologies4030037
Submission received: 29 July 2023 / Revised: 18 August 2023 / Accepted: 22 August 2023 / Published: 1 September 2023
Browse Figures
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Abstract

:
An animal’s ability to navigate its home range in search of essential resources is a key aspect of its ecology. To reach these resources, animals employ varying navigational processes depending on their exocentric or egocentric view of their environment. The goal of this study was to determine if the Javan slow loris (Nycticebus javanicus), a nocturnal arboreal primate found in southeast Asia, uses some form of cognitive map and spatial memory while navigating their environment. Using behavioural and GPS data of six males and seven females collected at the Little Fireface Project field station based in West Java, Indonesia, we measured their frequency of revisiting important feeding trees, route overlap, and points where individuals significantly changed directions. We found that all individuals predominantly used four tree species while feeding and foraging. The lorises also displayed a high level of route overlap, leading us to conclude that they likely utilize a route-based cognitive map where certain landmarks are integral to their nightly movement. Few studies have specifically focused on strepsirrhine spatial cognition in the wild; here, we show the navigational mechanism used by the Javan slow loris to reach distant/out-of-sight resources. The evident reliance on spatial cognition in a strepsirrhine species suggest that it could be an important selective pressure for primates at the earliest stages of primate cognitive evolution. In addition to the importance of spatial memory in theoretical discourse, understanding slow loris movement has practical applications to conservation, particularly regarding the numerous translocations undertaken by individuals rescued from the illegal wildlife trade. We discuss the importance of considering soft release training and monitoring in such releases.

1. Introduction

An animal’s ability to navigate its home range in search of essential resources is a key aspect of its ecology [1,2]. Finding these resources requires integration of spatial, temporal, and ecological factors, which may act as an important selection pressure on the evolution of spatial cognition [3,4]. Many animals maintain large home ranges that contain numerous potential feeding locations, many of which are not within sight of one another [1]. To reach these resources, animals will employ varying navigational processes depending on their exocentric or egocentric view of their environment [5]. The investigation of foraging strategies provides insights into the cognitive capacity of animals [6,7,8].
Byrne [9] and Garber [10] identified two types of cognitive maps, or mental representations, used by primates during foraging. Route based or topological cognitive maps are based on the spatial relation between objects [9,11]. This type of map implies that individuals have a representation of space, in which they are not able to visualize distance and direction to a desired location directly, but instead they must use landmarks along a route [12]. The Euclidean or vector-based cognitive map [9,12] allows the animal to perceive distance and direction from any point in the environment and is comparable to a Euclidean representation of space [11]. The inherently complex nature of identifying the mental state of animals makes it difficult to attribute any one navigational mechanism to observed movements. We can infer what mechanism is likely to be used, under the assumption that it will be the most efficient and cognitively available option. However, there is also evidence that some animals will use both cognitive maps based on the season [13,14] and distance to the goal [15].
Studies on spatial memory and foraging conducted across primate taxa showed that route-based maps are the most used form of cognitive map [16,17], with Euclidean maps being reported in chimpanzees [12,18,19,20], bonobos [21], and to an extent gorillas [22]. Despite their importance for reconstructing primate evolution and widespread interest in primate spatial ecology and cognition, only a handful of studies focus on strepsirrhines in the wild [3,13,23] and even fewer on nocturnal strepsirrhines [24,25].
The grey mouse lemur (Microcebus murinus) is the only nocturnal strepsirrhine with published large-scale navigational data in a wild context [24,25]. These lemurs were able to return to specific feeding locations and their movement patterns fitted with the expectations of a route-based cognitive map. In a captive setting, nocturnal primates’ navigation was explored in the fat-tailed dwarf lemur (Cheirogaleus medius), grey mouse lemurs and aye-ayes (Daubentonia madagascariensis). Each showed varying degrees of exploration and heuristics based on their dietary specialization: the more frugivorous dwarf lemur relied on heuristics and the least frugivorous aye-aye explored more [26]. Like grey mouse lemurs, the Javan slow loris (Nycticebus javanicus) displays the ability to adapt to changes in their environment, which is characterized by extreme anthropogenic disturbance [27]. The species also consumes a diet containing large proportions of exudates, nectar and insects [28], but the cognitive mechanism used to find and return to key immobile resources has not been identified. Gum is important as both a primary resource and a fallback food in at least 78 primate species [29]. Aspects of the Javan slow loris feeding ecology and postural ontogeny suggest that young individuals develop adult limb proportions to enable them to gouge and feed on tree gums [30]. If individuals develop to facilitate gouging and feeding on tree gum, it is also possible that they have efficient means to return regularly to these spatially dispersed feeding resources. The Javan slow loris thus provides an opportunity to explore further factors that may shed light on the origins of primate navigational mechanisms.
In this study, we examine the travel patterns of the Javan slow loris in a montane agroforest. To determine if Javan slow lorises use some form of cognitive map and spatial memory while navigating their environment, we addressed the following research questions: does the Javan slow loris display directedness during travel? If so, which type of navigational mechanism best characterizes their spatial representation of their environment? To determine if this species uses spatial memory, we assessed their nightly movement for directedness by measuring their reuse feeding trees, how they reach these feeding trees and other behaviourally important locations, and if there are places where directional changes take place. To rule out the possibility that they are reaching goal locations randomly, we also simulate nightly routes and compare the efficiency of each route at reaching feeding trees against routes each loris travelled. We hypothesized that Javan slow loris display directed travel patterns between resources. This hypothesis is supported if their nightly routes are reused creating a route network including nodes that connect multiple routes. Furthermore, we hypothesized that the Javan slow loris uses travel patterns best categorized as route-based cognitive maps, given it is the most prevalent cognitive map in the order Primates.

2. Materials and Methods

2.1. Study Species

The Javan slow loris is an approximately 1 kg non-leaping nocturnal primate found on the Indonesian island of Java in insular Southeast Asia (Figure 1). As a specialized exudate feeder, it relies heavily on immobile food resources consisting of gum and sap, but with nectar from flowers also being important [31]. The Javan slow loris lives in a uni-male/uni-female family group in home ranges of 5–10 ha, but largely forages solitarily [27,28]. For this study, we included 13 adult animals (M: 6, F: 7) that had established their home ranges for a year or more, from a well-known population that has been studied since 2011 [27].

2.2. Study Site and Data Collection

As a part of a long-term research project, we collected data for this study between January and December 2016 on a population of radio-collared slow lorises in Cipaganti, Garut District, West Java (7°6′6″ 7°7′ S and 107°46′ 107°46′5″ E). The area between the adjacent village and the protected forest is primarily patches of cultivated lands, bamboo, shrubs, and tree lines. The area where we conducted nightly observations encompassed 60 ha with elevations varying between 1200–1800 m asl [31].
We followed a protocol approved by the Animal Ethics Subcommittee at Oxford Brookes University. The radio collars (BioTrack, Wareham, UK) weighed 17 g, which is on average less than 2% of the body weight of an adult Javan slow loris and less than 4% of an immature Javan slow loris. We collected data through full- and half-night follows between 17:30 and 05:00. We identified individuals by a unique radio signal emitted from the radio collars worn by each study animal. During each shift we followed one focal study animal [33] and used a five-minute instantaneous point [34,35] to collect data on (1) the animal’s behaviour (i.e., alert, feed, forage, freeze, groom, rest, sleep, social, travel, other), (2) the tree species occupied, and (3) the GPS location of the individual (Garmin 64 s).

2.3. Data Analysis

We report all values as means ± standard deviation, and we accepted statistical significance when p ≤ 0.05 in two-tailed tests. To compare differences between sexes we used a Mann–Whitney U test, and Person’s correlation was used to test the relationship between home range size, overlap, and the percentage of revisited gum trees. To calculate the frequency of revisited gum-producing trees, we first defined unique gum-tree sites as trees that were 20 m or more apart to account for clusters and GPS error (5 m). If an individual visited the same cluster of trees on different nights, we noted that as a revisited tree. If multiple sites were less than 20 m apart, this was considered one revisited site.
We calculated each lorises’ home range in hectares (ha), representing the 95% kernel density estimation (KDE) and minimum convex polygon (MCP) in QGIS. We analysed all other collected geographical data in ArcGIS version 10.3. and R Programming 10.3. We used the straight-line distance between consecutive GPS points to determine the nightly route lengths for full- and half-night follows separately. Following a modified version of the methods used by Asensio et al. [15], we determined route overlap by calculating the length of overlapping routes, divided by the total length of all included routes. We defined overlapping routes as those that fell within a 5 m buffer, for at least 20 m [15,25], and these are reported as a percentage for each individual. During the overlap analyses, we excluded routes that had discontinuous points (those collected greater than 30 min apart).
We applied the change-point test (CPT), created by Byrne et al. [36], to nightly routes that included at least 15 waypoints, in R Programming 10.3. The CPT can determine locations where each route significantly changed direction, starting from the sleep site in the early evening. We recorded the behaviour before and after each change point to identify any potential behavioural associations.
We used simulated heuristic routes to test travel efficiency and to rule out that the slow lorises were randomly moving within their environment. The simulated routes started at the beginning of an actual travel bout and continued in a random direction until they encountered a gum-producing tree or the edge of the home range [15,25]. As part of the long-term study site, the locations of all the gum-producing trees previously visited by a slow loris as well as trees with visible gouge marks were recorded, regardless of size. To account for the varying distances an individual is able to identify and travel to a goal location through their senses (instead of using spatial memory alone), i.e., primarily vision, we added 5 m, 10 m, and 20 m buffers around each goal location while testing the efficiency of the simulated routes [25]. We defined the efficiency index as the number of goal locations visited by an individual subtracted by the number of goal locations found by the model, divided by the total number of goal locations visited within the home range. A negative value indicated that the model performed more efficiently than the study animal in finding goal locations, and a positive value indicated that the study animal was more efficient than the model [15]. We used a Kruskal–Wallis test to compare the efficiency index across detection radii.

3. Results

3.1. Nightly Route Length

Across all 13 individuals, the mean full-night route length (NPL) was 541 + 194 m, and the mean half-night NPL was 239 ± 53 m (Table 1). Males and females travelled comparable distances each night.

3.2. Visited Tree Diversity

Over the 12-month study period, slow lorises visited more than 20 types of trees within the area, though they predominantly revisited only four tree types: green wattle (Acacia decurrens), fairy duster (Calliandra spp.), bamboo (Bambusa spp.), and weeping paperbark (Melaleuca leucadendra). Within the proportion of sample points spent feeding and foraging, most of these points were spent at gum-producing trees, with 22.6 ± 12.6% (N = 565) spent in green wattle trees and 24.0 ± 10.3% (N = 502) in weeping paperbark. Additionally, 16.2 ± 7.0% (N = 347) of their time was spent in fairy duster trees, a nectar-producing site, and 18.0 ± 10.7% (N = 335) was spent in bamboo.

3.3. Revisited Gum-Producing Trees

Within each slow loris home range, individuals revisited gum-producing trees. They revisited 56.7 ± 13% of the unique gum trees visited throughout 2016 (Table 2). There was a significant positive correlation between the number of gum trees revisited and the home range size (r = 0.554, p = 0.0491).

3.4. Directional Changes and Route Overlap

We identified 140 change-points used during nightly travel (Table 2). Most change- points were associated with feeding (87%), sleeping (7%), and social (4%) behaviours. For two change-points, we were unable to identify any behaviourally meaningful associations. Of the 122 change-points associated with feeding behaviours, 104 were at gum-producing tree species and 18 were associated with nectar-producing fairy duster trees. Route overlap averaged 29 ± 8% per individual and ranged from 16% in individual TO to 44% in individual TE (Figure 2). Though there was no statistically significant difference between males and females, there was a trend where females displayed more overlap than males.

3.5. The Efficiency Index (EI)

The EI varied significantly across the detection radii (H = 569.5, df = 2, p < 0.0001); Javan slow lorises were more efficient than the model at the 5 m detection radius, performed similarly at 10 m, but were less efficient at the 20 m detection radius (Figure 3). Figure 4 presents an example of an actual and simulated route in one male Javan slow loris (AL).

4. Discussion

Based on the Javan slow loris’ frequent use of gum- and nectar-producing trees, we can see that traveling to feeding locations is an important aspect of their nightly movement. Throughout the night, individuals reused routes while traveling between goal locations and used change-points associated with those locations. The Javan slow loris outperformed the simulated routes in reaching feeding trees at the shortest detection radii, making it unlikely that they use random routes to routinely reach goal locations, but instead displays characteristics of a route-based cognitive map to reach distant immobile out-of-sight resources. Data presented here offer insight into the navigational capacity of the Javan slow loris, as resource distribution seems to structure their nightly movement.
It is important to rule out the possibility that animals are just using a random search model or happen upon needed resources [37]. The varying results produced by the efficiency index offer support towards rejecting the use of random search methods. The Javan slow loris outperformed the model in efficiency, reaching feeding resources within a 5 m detection radius; however, the model outperformed the real routes once the detection radius reached 20 m. Similar results were reported in mouse lemurs [25]. The perceptual range of slow loris species in this area is not known; thus, we were unable to assess to what degree visual, olfactory, or auditory cues may aid in reaching goal locations. However, it is known that all three of these environmental cues aid in foraging success [38,39,40,41]. Sensory cues work in concert to inform social, foraging, and movement behaviour [42].
Across primate species, including larger gregarious and small nocturnal species, the distribution of preferred feeding trees also structure the way individuals and groups move throughout their home ranges [15,25,43,44]. Tree exudates are a key food item for slow lorises [45]. Accessing tree exudates, such as gum, requires them to generate a hole and wait for the tree to repair the wound by producing the desired gums. Given the characteristics of this crucial resource, it is unlikely that slow lorises are monitoring feeding resources through visual or olfactory cues in the same ways that frugivores may [44]. The most plausible method that lorises use to revisit gum-producing trees and other behaviourally meaningful locations is to rely on their spatial memory. The fact that they revisit more than half of the unique tree sites within their home range further supports their use of spatial memory while foraging, as noted in other animals [46,47].
The Javan slow lorises in this population displayed route overlap comparable to other primates, including white-handed gibbons (Hylobates lar) (8–49%), grey mouse lemurs (9–30%), and saddleback tamarins (Saguinus fuscicollis) (32.4–74.2%) [15,25,48]. It is essential to highlight that the comparable level of overlap seen here is the result of non-consecutive observations. As a part of the 12-year study protocol, each individual was typically observed twice per month. Unlike the studies mentioned above, we did not complete consecutive multi-night observations but focused more on a longitudinal sampling method; thus, it is likely that the Javan slow loris uses a more robust route network than we detected in this study. However, we can conclude that the level of route reuse we did observe suggests that the Javan slow loris relies on the location of feeding sites and a route network to move through their home range, as is the case in other animals [49].
The presence of directional changes in association with goal resources suggests that the Javan slow loris uses the structural features of their landscape to reinforce travel decisions. When individuals reach landmark locations, they have more than one option past that point depending on the goal. When looking at cognitive map formation and use across other taxa, change-points can coincide with nodes or overlapping points within the route network where travel decisions are made routinely [1,50]. This principle is the primary assumption in justifying the importance of change-points in navigation by Byrne et al. [36]. Having a detailed cognitive map with several nodes and established routes aids movement and resource exploitation. Though highly flexible in their use and the ability to manoeuvre around their dynamic environment [27], the Javan slow loris likely uses landmarks to navigate to goal destinations, linking their survival to the landscape. In addition to the presence of a framework for their nightly travel, in a route-based map, the navigator needs the cognitive ability to integrate changing phenological data and have a sense of time to access and exploit changing resources accurately. We did not consider seasonality in this study but there is a known seasonality to diets of the slow lorises in this population [51]; therefore, further work is needed to fully understand how the route network changes with changing resources.

5. Translocation Implications

All slow lorises are globally threatened, including the Critically Endangered Javan slow loris, with a main threat to the genus being the illegal wildlife trade. For those that are confiscated, they are regularly translocated within and beyond their historical range [52,53,54,55,56]. Unfortunately, there is little data on their habitats to assist in the translocation work. In published work on slow loris translocations, habitat data is often absent [52], described in vague terms [57], or primarily focused on forest connectivity and not the spatial distribution of key resources regarding feeding and foraging [53,54,55,56]. If Javan slow lorises build a cognitive map of their environment based on the locations of key resources, characterizing resource distribution should be a key component in planning translocations. In addition, it takes time to build a cognitive map. Since post-release success is predicated on “survival” within a predetermined window, we must consider the timeframe needed for a species to familiarize themselves with their environment. Further studies on how slow lorises develop their cognitive maps could help redefine the standard timeframe by which we can measure the success of translocations. Indeed, in a study by Campera et al. [58], when comparing dispersing animals who had opportunities to familiarize themselves with potential dispersal sites with translocated ones in the same area, natal dispersers had higher survival rates and settled more quickly.
Habitat quality is an essential component of translocation success [59,60] and is most cited as the cause of translocation failures [61,62,63]. Translocations can fail when a released animal cannot acclimate itself to a new environment [64,65], or rejects a site and disperses [66,67]. Not only should we thoroughly assess the presence of essential vegetation in potential release sites, but we should also measure the spatial distribution and density of key resources. The degree to which individual trees are aggregated or dispersed is crucial to how an individual will utilize their new area. Selecting a site with an insufficient distribution of key resources may limit the number of individuals that can be translocated, especially in territorial species like the Javan slow loris. Any frequent structural changes should also be noted. As a probable route-based map user, landmarks are a crucial aspect of the way these primates envision their environment. If the potential release site is part of an anthropogenically modified area, the translocated slow lorises may face difficulty in creating a complete mental representation from which to root their umwelt, thus leading to failed translocations efforts. Despite the challenges of translocations, they can be a powerful tool for conserving wild populations if researchers can determine if the target individuals are able to find and relocate key resources to survive, especially in species that rely on spatially restricted resources.

Author Contributions

Conceptualization, S.A.P. and K.A.-I.N.; methodology, S.A.P.; software, S.A.P.; validation, S.A.P.; formal analysis, S.A.P.; investigation, K.A.-I.N. and S.A.P.; resources, K.A.-I.N.; data curation, K.A.-I.N.; writing—original draft preparation, S.A.P.; writing—review and editing, S.A.P., K.A.-I.N. and V.N.; visualization, S.A.P.; supervision, K.A.-I.N., M.A.I. and V.N.; project administration, K.A.-I.N., V.N. and M.A.I.; funding acquisition, K.A.-I.N. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by Augsburg Zoo, Cleveland Zoo and Zoo Society, Conservation International Primate Action Fund and Margot Marsh Biodiversity Fund, Disney Worldwide Conservation Fund, International Primate Protection League, Lee Richardson Zoo, Memphis Zoo, Mohamed bin al Zayed Species Conservation Fund (152511813), Moody Gardens Zoo, NaturZoo Rhein, Omaha’s Henry Doorly Zoo, People’s Trust for Endangered Species, Plumploris E.V., Primate Society of Great Britain, San Francisco Zoo, Shaldon Wildlife Trust, Sophie Danforth Conservation Biology Fund and Zoo De Lille.

Institutional Review Board Statement

All research was approved by the Animal Care Subcommittee of Oxford Brookes University number OBURASC0911 and adhered to the ASAB/ABS Guidelines for the Use of Animals in Research. All necessary research permits were obtained from the Indonesian government. All research adhered to the legal and ethical guidelines of the Indonesian Institute of Sciences, Department of Wildlife and Department of Forestry.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Acknowledgments

We thank the Little Fireface Project field team for their help in radio tracking slow lorises, data collection, and aid in local logistics. We thank the three reviewers and the editor for their very useful comments and the Loris Lab at Oxford Brookes University for their comments on this manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. (a) Map of the study location within Indonesia, and (b) a satellite image of the montane agroforest where this study population was observed. This map was reproduced from [32].
Figure 1. (a) Map of the study location within Indonesia, and (b) a satellite image of the montane agroforest where this study population was observed. This map was reproduced from [32].
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Figure 2. Line density heatmap of the nightly routes for one female Javan slow loris (TE) throughout the study period, within their home range (grey 95% KDE). The overlapping route network is presented in black (routes travelled more than five times), grey (three to four times), and dotted (twice). The black circles are junctions or nodes where multiple routes intersected over multiple days. In grey are all the recorded routes.
Figure 2. Line density heatmap of the nightly routes for one female Javan slow loris (TE) throughout the study period, within their home range (grey 95% KDE). The overlapping route network is presented in black (routes travelled more than five times), grey (three to four times), and dotted (twice). The black circles are junctions or nodes where multiple routes intersected over multiple days. In grey are all the recorded routes.
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Figure 3. Efficiency indices measured in 13 Javan slow lorises (Nycticebus javanicus) in relation to the detection radii.
Figure 3. Efficiency indices measured in 13 Javan slow lorises (Nycticebus javanicus) in relation to the detection radii.
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Figure 4. Map of feeding locations visited by a male Javan slow loris (Nycticebus javanicus) and its home range (calculated using a 95% minimum convex polygon). Feeding sites are represented by tree icons, which vary in size based on the number observation points recorded in that area. The individual’s route is represented by the black line starting at the star and ending at the ‘X’. One route produced using a heuristic model at the five-metre detection radius is represented by the dotted line, starting at the star, and ending at the ‘X’. Along the actual route the encircled ‘X’ denotes change-points. The black line denotes 300 m.
Figure 4. Map of feeding locations visited by a male Javan slow loris (Nycticebus javanicus) and its home range (calculated using a 95% minimum convex polygon). Feeding sites are represented by tree icons, which vary in size based on the number observation points recorded in that area. The individual’s route is represented by the black line starting at the star and ending at the ‘X’. One route produced using a heuristic model at the five-metre detection radius is represented by the dotted line, starting at the star, and ending at the ‘X’. Along the actual route the encircled ‘X’ denotes change-points. The black line denotes 300 m.
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Table 1. Full- and half-night route lengths (meters), homerange size using kernel density estimates and minimum convex polygon (hectare) in each adult male and female Javan slow loris denoted by their initial.
Table 1. Full- and half-night route lengths (meters), homerange size using kernel density estimates and minimum convex polygon (hectare) in each adult male and female Javan slow loris denoted by their initial.
HR (KDE)HR (MCP)Full-Night Half-Night
NMeanMaxMinNMeanMaxMin
MaleAC3.2712.4----6250.6686.1107.9
AL6.69.55733.21121.7485.525275.81582.673.2
AZ5.08.06417.8684.6133.612190.4307.196.3
FE12.714.43668.1811.4414.620257.5627.793.7
RA6.220.71516.5516.5516.517192.5317.961
TO4.59.34317.937728913189.9355.363.1
FemaleEN3.67.72578.6731.9425.37332.2471.9198.5
LU6.39.734513.5695.8328.420162.3406.223.3
MA3.66.46626.41264360.914253.4511.783.6
OE4.15.41428.2428.2428.212202.7418.473
SH2.43.87613.9871.8400.514238401.7113
SI4.1211.0----11328523.6156.8
TE4.96.55555.7968380.221205450.933.6
Total 541.4 239.4
Table 2. The number of change-points present in the nightly routes, the percentage of their route that overlapped, and the number of unique gum-producing trees visited and revisited for each Javan slow loris included in the study.
Table 2. The number of change-points present in the nightly routes, the percentage of their route that overlapped, and the number of unique gum-producing trees visited and revisited for each Javan slow loris included in the study.
SexIDNumber of CPRoute Overlap# Unique Sites (Revisited)Percentage Revisited
FemaleLU1435%37 (28)76%
TE1344%60 (29)48%
MA1132%38 (24)63%
EN1030%21 (9)43%
SI925%36 (15)42%
OE839%34 (17)50%
SH522%35 (15)49%
MaleAL1837%40 (32)80%
FE1519%67 (44)66%
RA1218%25 (15)60%
TO1116%35 (18)52%
AZ828%29 (13)45%
AC632%14 (5)36%
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MDPI and ACS Style

Poindexter, S.A.; Nijman, V.; Imron, M.A.; Nekaris, K.A.-I. Goal-Directed Travel in the Nocturnal Javan Slow Loris (Nycticebus javanicus). Ecologies 2023, 4, 568-579. https://doi.org/10.3390/ecologies4030037

AMA Style

Poindexter SA, Nijman V, Imron MA, Nekaris KA-I. Goal-Directed Travel in the Nocturnal Javan Slow Loris (Nycticebus javanicus). Ecologies. 2023; 4(3):568-579. https://doi.org/10.3390/ecologies4030037

Chicago/Turabian Style

Poindexter, Stephanie A., Vincent Nijman, Muhammed Ali Imron, and K. Anne-Isola Nekaris. 2023. "Goal-Directed Travel in the Nocturnal Javan Slow Loris (Nycticebus javanicus)" Ecologies 4, no. 3: 568-579. https://doi.org/10.3390/ecologies4030037

APA Style

Poindexter, S. A., Nijman, V., Imron, M. A., & Nekaris, K. A. -I. (2023). Goal-Directed Travel in the Nocturnal Javan Slow Loris (Nycticebus javanicus). Ecologies, 4(3), 568-579. https://doi.org/10.3390/ecologies4030037

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