velazquez-vazquez etal 2015 ijp sleepingsites

8/18/2019 Velazquez-Vazquez Etal 2015 IJP SleepingSites

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Sleeping Sites of Spider Monkeys ( Ateles geoffroyi )

in Logged and Unlogged Tropical Forests

Guadalupe Velázquez-Vázquez1 & Rafael Reyna-Hurtado1 &

Victor Arroyo-Rodríguez2 & Sophie Calmé1,3 &

Mathieu Léger-Dalcourt3 & Darío A. Navarrete4

Received: 3 August 2015 /Accepted: 27 October 2015 / Published online: 15 December 2015

# Springer Science+Business Media New York 2015

Abstract Selective logging can have negative effects on biodiversity and on key

ecological processes such as seed dispersal and forest regeneration. Yet, the effect that

timber extraction has on animal behavior and habitat use is poorly known. We tested

whether the density, distribution, and composition of sleeping sites used by spider

monkeys ( Ateles geoffroyi) differed between two logged and two unlogged forest sites

in the Calakmul region, southeastern Mexico. We recorded a total of 74 sleeping sites

(0.11 sleeping sites/ha). The density of sleeping sites did not differ between forest conditions. Most (97%) sleeping sites were located in medium-stature semievergreen

forest, and only 3% in low-stature seasonally inundated forest. In three of four sites, the

number of sleeping sites in core areas was significantly greater than expected by

chance, showing an aggregated spatial distribution, particularly in areas containing a

greater density of feeding trees. About half (51%) of the sleeping sites were composed

of a single large tree (mean ± SD diameter at breast height, 42.2 ± 21.9 cm) from a

small number of tree species, such as Lonchocarpus castelloi, Bucida buceras, and

Lysiloma latisiliquum. These results suggest that the current level of timber extraction

seems to have no effect on the density, distribution, and composition of sleeping sites.

Nevertheless, because the species that were selected as sleeping trees are subject to

Int J Primatol (2015) 36:1154 – 1171

DOI 10.1007/s10764-015-9883-8

* Guadalupe Velázquez-Vázquez

[email protected]

1Departamento de Conservación de la Biodiversidad, El Colegio de la Frontera Sur (ECOSUR),

Unidad Campeche, CP 24500 Campeche, México2

Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de

México (UNAM), Morelia 58190 Michoacán, México

3 Département de Biologie, Université de Sherbrooke, Quebec J1K 2R1, Canada

4Laboratorio de Información Geográfica, El Colegio de la Frontera Sur (ECOSUR), San Cristóbal

de Las Casas CP 29290 Chiapas, México

http://crossmark.crossref.org/dialog/?doi=10.1007/s10764-015-9883-8&domain=pdf


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timber extraction, the availability of sleeping sites is expected to decrease in coming

decades, potentially modifying the habitat use of this primate species.

Keywords Home range . Latrine . Neotropical primates . Seed dispersal . Sleeping tree .

Timber extraction

Introduction

Selective logging is a pervasive driver of forest degradation across the tropics (Asner

et al . 2009), with negative effects on many plant and animal species (Fredericksen and

Fredericksen 2002; Heydon and Bulloh 1997; Putz et al . 2001; cf. Berry et al . 2010;

Bicknell et al . 2015), including primates (Gutiérrez-Granados and Dirzo 2010; Johns

1992). Logging can also alter soil properties and forest regeneration (Malhi et al . 2014;Putz et al . 2001), as well as ecological interactions such as frugivory, seed dispersal,

and seedling recruitment (Gutiérrez-Granados and Dirzo 2010; Johns 1992).

Nevertheless, the effect that timber extraction may have on animal behavior

and habitat use is largely unknown, particularly for primates (cf. Chapman et

al . 2000). This information is required to design effective conservation strate-

gies, particularly considering that timber extraction is an economically impor-

tant activity that is becoming increasingly common in tropical forests world-

wide (Ariyanti et al . 2008; Asner et al . 2005; Berry et al . 2010; Malhi et al .

2014).Timber extraction can modify the tree canopy, potentially altering resource avail-

ability and habitat use for primates that are primarily tree dwellers. Sleeping sites are an

important resource for many tree-dwelling primates, which depend on suitable sleeping

sites for their daily activity patterns and survival (Anderson 1998; Smith et al . 2007).

The selection of sleeping sites by primates depends on many factors. For example,

night or owl monkeys ( Aotus spp.), gibbons ( Hylobates lar ), and spider monkeys

( Ateles geoffroyi) select sleeping sites that are composed of one or several closely

spaced large trees with dense foliage in which to sleep, most probably as a strategy for

hiding from predators and as protection from adverse climatic conditions (Aquino and

Encarnación 1986; González-Zamora et al . 2012; Reichard 1998). Also, many primate

species select sleeping sites that are located near available food resources to reduce

travel costs and increase foraging efficiency ( Ateles geoffroyi: Asensio et al . 2012a ;

Chapman 1989; González-Zamora et al . 2014; Callithrix jacchus: Mendes-Pontes and

Lira 2005; Collobus vellerosus: Teichroeb et al . 2012; Saguinus spp.: Smith et al .

2007). Thus, the loss and alteration of sleeping sites as a consequence of selective

logging could limit the persistence of primates in human-modified tropical landscapes,

but to our knowledge, no study to date has tested the effect of timber extraction on

primate selection of sleeping sites.

Here, we tested whether the density, distribution, and composition of sleeping sites

used by spider monkeys ( Ateles geoffroyi) differed between logged and unlogged

forests in the Calakmul region, southeastern Mexico. In this region, ca. 6500 km2 are

devoted to timber extraction following de facto norms established by the Forest

Stewardship Council (FSC 1996) regarding forest management, e.g., minimizing

damage to remaining trees, soil, and water bodies, ensuring tree regeneration,

Sleeping Sites of Ateles geoffroyi 1155


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diversifying timber production). The effects that such management practices may have

on primates are unknown.

Spider monkeys show fission – fusion social dynamics (Aureli and Schaffner 2008),

i.e., they split to forage in subgroups of variable size and composition during the day,

and may congregate in larger subgroups during the evening to sleep in sleeping sitesthat are formed by one or several sleeping trees (Chapman 1989; González-Zamora

et al . 2012, 2014; Russo and Augspurger 2004). As they frequently return to the same

sleeping sites after feeding excursions, they are considered Bmulti-central place

foragers^ ( sensu Chapman et al . 1989). Spider monkeys deposit scats containing seeds

along their daily routes and deposit copious amounts of dung and seeds in latrines that

are located beneath sleeping sites (González-Zamora et al . 2012, 2014, 2015; Russo et

al . 2006). For example, González-Zamora et al . (2014) found that 8 communities of

spider monkeys deposited >45,000 seeds (>5 mm in length) from 68 plant species

during a 13-mo period. Although seed aggregation in latrines can increase seedmortality (Connell 1971; Janzen 1970), the enormous accumulation of seeds and

seedlings in latrines could saturate seed and seedling predators, thus allowing some

seeds and seedlings to escape predation and recruit in latrines (Bravo 2012). Thus,

spider monkey latrines may represent hotspots of seedling recruitment, thereby having

a key role in forest regeneration (González-Zamora et al . 2014, 2015; Russo and

Augspurger 2004; Russo and Chapman 2011). Therefore, understanding the impact

that selective logging may have on the density, spatial distribution, and composition of

sleeping sites has critical ecological and conservation implications. Such information is

particularly urgent because spider monkeys are increasingly forced to inhabit human-modified landscapes, which are subject to timber extraction (Ramos-Fernández and

Wallace 2008).

We hypothesized that timber extraction limits the availability of suitable sleeping

trees. More specifically, we predicted that the density of sleeping sites would be lower

in logged than in unlogged forests. We also predicted that the distribution of sleeping

sites would be spatially more dispersed in logged forests than in unlogged forests,

because food resources may be less abundant and spatially more dispersed in logged

forests than in unlogged forests. Finally, we predicted that monkeys would rely on

fewer tree species and smaller trees in logged forests than in unlogged forests,

particularly if sleeping trees were subject to timber extraction.

Methods

Study Area

We performed the study in the Nuevo Becal ejido, a communally shared area adjacent

to the Calakmul Biosphere Reserve, Calakmul Municipality, Campeche, Mexico

(18°40′07.7′′ N, 89°12′34.3′′W) (Fig. 1). This ejido has an area of 520 km2, of which

250 km2 are devoted to timber extraction. The climate is warm and sub-humid, with

annual temperatures averaging 27°C, and annual precipitation ranging from 600 to

1200 mm (García et al . 2002). A dry season occurs from December to May, and a rainy

season from June to November. The vegetation is composed mainly of medium-stature

semievergreen forest, with trees reaching heights of 20 – 25 m, and low-stature

1156 G. Velázquez-Vázquez et al.


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Figure 1 Location of study sites in Campeche State, Mexico. Small polygons with black outlines represent

the home ranges of four groups of spider monkeys for five consecutive months in 2014. The groups are

located in two logged forests (L1 and L2) and two unlogged forests (C1 and C2). Spatial distribution of

sleeping sites (▲) and feeding trees (+) used by the four groups of spider monkeys in their home ranges are

also indicated. We estimated home ranges using the minimum convex polygon method (black line) and the

fixed kernel method at 95% (gray line) and 50% (white line).



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seasonally inundated forest, with trees


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considered all latrines that were located


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recording it. To estimate the availability of trees that could be potentially used as

sleeping trees, we recorded all trees with a DBH ≥10 cm in 30 randomly distributed 50

m × 2 m plots in C1 and L1 (3000 m2 of sampling area per site), and in 74 circular plots

each of 10-m radius in sites C2 and L2 (23,247 m2 of sampling area per site). We used

these two types of plots because they were designed for estimating variables not included in the present study. Yet, such differences did not bias our results, as we

included both types of plots in both unlogged and logged forests.

We estimated inter-sleeping site distances with the Hawth’s Tools-Nearest Neighbor

extension for ArcGIS 9.3 (Beyer 2004). The analysis was made with the extension

Spatial Statistics and Analyzing Patterns with the tool Average Nearest Neighbor. To

evaluate the spatial distribution of sleeping sites, we used the Average Nearest-

Neighbor (ANN) index:

ANN ¼ Do

DE

where Do is the average distance between each sleeping site and its closest neighbor,

and DE is the average expected distance, expressed as a function of a random pattern.

Do ¼X N

i¼

1

d i

DE ¼0:5 ffiffiffiffiffiffiffiffiffi N = A

p

where d i is the distance between sleeping site i and its nearest neighboring sleeping site,

N is the total number of sleeping sites, and A is the area that encapsulates all recorded

sleeping sites. Values of ANN 1

indicate a dispersed pattern (Mitchell 2005).

Home Range of Spider Monkeys

We used the minimum convex polygon (MCP) and fixed kernel methods to estimate

the home range (MCP and fixed kernel) and core areas (fixed kernel) of each commu-

nity. MCP is used mainly for comparative purposes as it is widely employed, and

because it represents the entire area that animals can visit. The fixed kernel is based on

the density distribution of primate locations, and thus represents more intensively used

areas (Kernohan et al. 2001). The smoothing factor calculated for kernel estimations

was calculated in an iterative way as recommended by Kernohan et al . (2001), as the

least square cross validation method tends to underestimate it. We used the same

smoothing factor for analysis of the four groups. Fixed kernel home ranges were

defined as the 95% probability contour and core areas as the 50% probability contour.

We estimated distances between sleeping sites with the Spatial Statistics and Analyzing

Patterns extension and the Average Nearest Neighbor tool in Hawth’s Tools-Nearest

Neighbor extension for ArcGIS 9.3 (Beyer 2004). Despite the short sampling period,



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we found that the home range size-sampling effort curves also reached an asymptote in

all groups (Fig. 2).

Distribution of Sleeping Sites and the Availability of Feeding Trees

To test if the distribution of sleeping sites was related to the availability of feeding trees,

we determined the number of feeding trees in the Moore neighborhood of sleeping

sites, together with a similar number of sites that were selected at random in the home

range of the community. The Moore neighborhood consists of nine cells in a lattice,

viz., one central cell (in our case, containing sleeping sites or locations chosen

randomly) and its eight surrounding cells (Weisstein 2005). To calculate these neigh-

borhoods, we first constructed a grid with a cell size of 1 ha on the map of home ranges

(Fig. 1). This cell size is smaller than the mean distance reportedly traveled between

feeding trees by spider monkeys (162 ± 86 m, rainy season, and 393 ± 54 m, dryseason: Nunes 1995). This allowed feeding trees to be spread over several cells, making

possible the estimation of neighboring effects. We then calculated the number of

feeding trees in each of the eight cells that neighbored the cells that contained sleeping

sites and in randomly selected cells (where no sleeping site had been detected) with the

Hawth’s Tools-Sampling Tools function in ArcGis 9.3 (Beyer 2004).

Statistical Analyses

To test for differences between logged and unlogged forests in terms of thedensity of sleeping sites and mean inter-sleeping site distances, we used Student

t -tests, which can be used with extremely small sample sizes (de Winter 2013).

To test for differences among sites in the size (DBH) of sleeping trees, we used

a one-way analysis of variance. We also used Student t -tests to evaluate if the

DBH of sleeping trees was significantly higher than the DBH of available trees

in the home range (one analysis per species). Lastly, we used simple logistic

regression to test if the presence of sleeping sites is positively related to the

abundance of feeding trees in its Moore neighborhood. These statistical analy-

ses were performed in SPSS v. 17.0 (SPSS Inc., Chicago). We also used chi

square goodness-of-fit tests using HABUSE (Byers et al . 1984) to assess

whether the observed number of sleeping sites and feeding trees within and

outside core areas differed from to that expected by chance. We calculated the

expected values based on the area available within and outside core areas.

To identify spider monkey preferences for specific sleeping tree species, we

used Manly’s standardized selection index (Manly et al . 2002), which is one of

the simplest and most effective methods for measuring resource preference. It is

based on the selection ratio wi, which is defined as the proportional use divided

by the proportional availability of each resource: i.e., wi = oi/ π i, where oi is the

proportion of the sleeping trees of species i that are used, and π i is the

proportion of available trees of species i in the home range (see vegetation

sampling in the preceding text). A wi value >1 indicates positive selection,


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Ethical Note

We obtained all of the necessary permits that were required for the present study.

Because our research involved an observational field study and did not involve any

contact with the animals, we met all ethical and legal requirements that were established by the Principles for the Ethical Treatment of Non-Human Primates of the International

Primatological Society. This study was also approved by the authorities from

ECOSUR, Université de Sherbrooke, Reserva de la Biosfera de Calakmul, and

Consejo Nacional de Ciencia y Tecnología (CONACYT-Mexico), and has the autho-

rization of the owners of the study area.

Results

Density of Sleeping Sites

In total, we recorded 74 sleeping sites in 668.7 ha of the fixed kernel polygon sampling

area, i.e., 0.11 sleeping sites/ha. The density of sleeping sites did not differ significantly

between logged and unlogged sites (t -test; t = 1.66, df = 1, P = 0.24). Estimated

densities were respectively 0.11 and 0.16 sleeping sites/ha in L1 and L2, and 0.04 and

0.10 sleeping sites/ha in C1 and C2.

Spatial Distribution of Sleeping Sites and Feeding Trees

In all study sites, sleeping sites had an aggregated distribution (C1: ANN = 0.74, P <

0.01; C2: ANN = 0.92, P = 0.05; L1: ANN = 0.43, P < 0.01; L2: ANN = 0.68, P <

0.01). Most sleeping sites (61%) were separated from one another by distances


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Table I Home ranges and core areas (both in hectares) of spider monkeys in January – May and March –

July 2014 in two logged (L1 and L2) and two unlogged (C1 and C2) sites in Calakmul, Mexico

Sites Home

range

Core

area

In core areas χ2 P Outside core areas χ2 P

Obs. Exp.

C1 202.0 24.5 SS 8 (+) 2.66 10.7


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Characteristics of Sleeping Sites

About half (51%) of the sleeping sites were composed of a single sleeping tree and

showed only one latrine (Table III). The mean (± SD) DBH of sleeping trees was 42.2 ±

21.9 cm, and did not differ among sites ( F 3,127 = 1.28, P = 0.28). In total, we recorded128 sleeping trees from 21 species, with more than half (55%) of the trees belonging to

three species: Bucida buceras L. (Combretaceae), Lonchocarpus castelloi Stanley

(Fabaceae), and Lysiloma latisiliquum (L.) Benth. (Fabaceae) (Table III). Spider mon-

keys actively selected some of the tree species, such as B. buceras and L. castelloi in

C1, an unidentified species (locally called Bcelestillo^), and L. latisiliquum in C2;

Talisia oliviformis (Kunth.) Radlk. (Sapindaceae) and Swietenia macrophylla

(Meliaceae) in L1; and Bcelestillo^ and L. castelloi in L2 (Table IV). In most cases,

sleeping trees exhibited a DBH greater than those of conspecific trees that were not

used as sleeping trees in their respective home ranges, yet the difference was not significant for all species (Table IV).

Table III Abundance and density of sleeping sites (SS) used by spider monkeys in January – May and March –

July 2014 in two unlogged (C1 and C2) and two logged (L1 and L2) sites in Calakmul, Mexico

Sites Abundance Density (SS/ha) Characteristics of SS Number (%) of SS

NST NL

C1 23 0.10 1 1 9 (39.1)

2 2 4 (17.4)

2 1 3 (13.0)

2 3 2 (8.7)

1 2 2 (8.7)

4 4 1 (4.4)

4 5 1 (4.4)

2 3 1 (4.4)

C2 7 0.04 1 1 6 (85.7)

4 4 1 (14.3)

L1 16 0.11 1 1 8 (50.0)

2 2 4 (25.0)

3 4 2 (12.5)

2 3 1 (6.3)

2 1 1 (6.3)

L2 28 0.16 1 1 15 (53.6)

3 3 6 (21.4)2 1 4 (14.3)

2 2 2 (7.1)

4 4 1 (3.6)

We classified SS in each forest site based on the number of sleeping trees (NST) and latrines (NL) that

composed each SS and we then indicate the number (and %) of SS in each class.



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Table IV Use and availability of tree species for spider monkey sleeping sites in January – May and March –

July 2014 in two unlogged (C1 and C2) and two logged (L1 and L2) sites in Calakmul, Mexico

Sites and tree species No. of trees

(and density)

wi Mean DBH (and range) (cm) t P

Used Available Used Available

C1

Sickingia salvadorensis 1 (4.95) 7 (0.23) 0.56 30.6 17.7 (12.0 – 27.7) — —

Swietenia macrophylla 7 (3.56) 10 (0.33) 2.78 40.2 (28.3 – 51.9) 37.7 (28.0 – 45.2) 0.68 0.50

Bucida buceras 9 (4.45) 4 (0.13) 8.94 59.5 (25.5 – 114) 51.4 (36.3 – 83.7) 0.54 0.59

Lonchocarpus castelloi 4 (1.98) 4 (0.13) 3.98 43.9 (40.4 – 47.4) 21.6 (12.4 – 33.1) 4.17 0.03

Pouteria spp. 4 (1.98) 60 (2.00) 0.27 33.5 (29.9 – 35.0) 17.0 (10.2 – 31.2) 5.85 0.00

Caesalpinia gaumeri 6 (2.97) 7 (0.23) 3.41 36.4 (23.9 –

42.0) 32.7 (11.5 –

67.7) 0.48 0.63 Brosimum alicastrum 1 (4.95) 46 (1.53) 0.09 49 27 (10.2 – 151.8) — —

Exothea diphylla 4 (1.98) 5 (0.17) 3.18 26.3 (22.9 – 33.4) 15.4 (10.5 – 20.7) 3.48 0.01

Ficus cotinifolia 2 (9.90) 7 (0.23) 1.14 29.1 (22.0 – 36.3) 28.4 (10.8 – 50.0) 0.06 0.95

Cosmocalyx spectabilis 1 (4.95) 4 (0.13) 0.99 22.1 24.2 (18.4 – 35.0) — —

Vitex gaumeri 1 (4.95) 5 (0.17) 0.80 82.1 24.0 (12.4 – 37.2) — —

C2

Lonchocarpus castelloi 1 (6.18) 16 (0.06) 0.15 24.4 28.9 (10.6 – 57.2) — —

Lysiloma latisiliquum 3 (1.85) 3 (0.01) 2.50 32.7 (28.0 – 41.2) 48.2 (39.2 – 63.6) 1.76 0.15

Unidentified 4 (2.47) 1 (0.00) 10.00 37.8 (25.9 – 61.8) 21.1 — —

Logged sites

L1

Brosimum alicastrum 5 (3.46) 40 (1.33) 0.40 51.9 (30.4 – 72.9) 32.2 (10.2 – 76.4) 2.41 0.02


Talisia oliviformis. 2 (2.00) 3 (0.10) 3.21 27.1 (23.4 – 29.9) 25.5 (14.5 – 42.0) 1.86 0.86

Bucida buceras 3 (2.00) 7 (0.23) 1.37 33.7 (25.4 – 40.7) 30.8 (15.6 – 65.8) 0.26 0.79

Ficus sp. 4 (2.77) 6 (0.20) 2.14 72.9 (29.6 – 142.6) 18.6 (10.2 – 29.6) 2.47 0.03

Swietenia macrophylla 3 (2.00) 3 (0.10) 3.21 36.9 (28.0 – 42.7) 38.4 (35.0 – 43.3) 0.29 0.78

Lysiloma latisiliquum 1 (6.93) 2 (0.07) 1.60 148.0 34.1 (30.1 – 38.2) — —

Exothea diphylla 1 (6.93) 8 (0.27) 0.40 31.6 16.9 (10.2 – 31.8) — —

Protium copal 1 (6.93) 13 (0.43) 0.24 14.3 18.9 (10.3 – 41.5) — —

L2

Manilkara zapota 1 (6.23) 93 (0.40) 0.12 38.6 34.4 (10.2 – 78.8) — —

Bucida buceras 4 (2.49) 24 (0.10) 1.81 44.2 (22.5 – 90.5) 35.1 (11.3 – 87.9) 0.76 0.45

Caelsalpinia mollis 2 (1.25) — 26.5 (23.1 – 29.9) — — —

Caesalpinia gaumeri 1 (6.23) 4 (0.02) 2.71 23.2 48.1 (34.3 – 62.0) — —

Ficus sp. 3 (1.87) 59 (0.26) 0.55 51.3 (20.0 – 79.3) 34.7 (12.2 – 153.0) 1.26 0.21

Licaria peckii 1 (6.23) 12 (0.05) 0.90 30.0 19.7 (10.2 – 39.4) — —


Lysiloma latisiliquum 10 (6.23) 12 (0.05) 9.04 41.7 (16.2 – 53.8) 32.3 (12.1 – 61.0) 1.51 0.14

Unidentified 4 (2.49) 1 (0.00) 43.37 37.8 (25.9 – 61.8) 21.1 — —

Pouteria sp. 1 (6.23) 130 (0.57) 0.08 24.1 15.0 (10.0 – 40.3) — —



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Discussion

Our findings reveal that there are 0.11 sleeping sites/ha in the study region;

almost all of them (97.2%) were located in medium-stature semievergreen forest.

In all study sites, sleeping sites showed an aggregated distribution, particularly incore areas within their home range, which contained a greater density of feeding

trees. About half of the sleeping sites were composed of one single large tree

from a small number of tree species, particularly from Lonchocarpus castelloi,

Bucida buceras, and Lysiloma latisiliquum. These three species represented one-

third of the authorized volume for logging during the period when the sites were

logged, and in 2007 they made up 50% of the harvested volume. However,

contrary to our predictions, the density, distribution, and composition of sleeping

sites did not differ between logged and unlogged forests, suggesting that current

levels of timber extraction had no effect on the sleeping site characteristics that

we measured.

The diet of spider monkeys is composed mainly of fruits (Di Fiore et al . 2008;

González-Zamora et al . 2009), the availability of which is strongly variable in time and

space. To reduce travel costs and increase their foraging efficiency, spider monkeys

usually sleep close to their feeding areas (Chapman et al . 1989). This means that the

density and distribution of sleeping sites can be highly variable across sites (Asensio

et al . 2012a ; González-Zamora et al . 2012), depending on the distribution and avail-

ability of food resources (Asensio et al . 2012b; Chapman et al . 1989; González-Zamora

et al . 2015). Consistent with these ideas, we found that the density of sleeping sites was

strongly variable across sites (ranging from 0.04 to 0.16 sleeping sites/ha), but most

sleeping sites were distributed in medium-stature semievergreen forest, which is the

vegetation type with the greatest fruit availability, i.e., with larger trees and a greater

number of plant species consumed by mammals, in the region (Reyna-Hurtado et al .

2009). Also, the occurrence of sleeping sites was positively related to the density of

feeding trees, and most sleeping sites were found in the cores of home ranges, which

Table IV (continued)

Sites and tree species No. of trees

(and density)

wi Mean DBH (and range) (cm) t P

Used Available Used Available

Protium copal 1 (6.23) 37 (0.16) 0.29 19.3 16.3 (10.2 – 85.6) — —

Pseudobombax ellipticum 3 (1.87) 11 (0.01) 2.96 69.4 (52.2 – 80.3) 69.4 (12.8 – 80.3) 2.49 0.02

Simarouba glauca 1 (6.23) 9 (0.04) 1.20 29.2 20.0 (11.7 – 47.1) — —

Swartzia cubensis 2 (1.25) 13 (0.06) 1.67 26.7 (23.8 – 29.6) 33.3 (12.1 – 77.2) 0.48 0.63

Swietenia macrophylla 1 (6.23) 5 (0.00) 2.17 51.4 31.6 (18.9 – 43.8) — —

The density of trees is expressed as the number of individuals/sampled surface area. A Manly index (wi) in

bold indicates selection by spider monkeys. We also indicate the results of the Student ’s t — test used to identify

differences in diameter at breast height (DBH) between used and available trees (the cases in which thisanalysis was not possible are indicated with a dash).



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The density of sleeping sites in our study area (0.11 sleeping sites/ha) was far

below the figures reported by González-Zamora et al . (2012) for the Lacandona

rainforest, Mexico (0.25 sleeping sites/ha). This may be related to the fact that spatial and temporal variation in fruit availability is greater in medium-stature

semievergreen forests than in rainforests (A. Aguilar-Melo et al ., unpub. data),

reinforcing the observation that sleeping sites are spatially more dispersed in

semievergreen forests than in rainforests. In fact, the temporal scarcity of fruiting

trees in seasonal forests can lead spider monkeys to stay near feeding trees until

they are depleted (Chapman 1987). This may explain why the density of sleeping

sites is notably lower in seasonal medium-stature semievergreen forests than in

nonseasonal tropical rainforests. Further, we cannot rule out the possibility that

differences in sleeping site densities were caused by differences in the definitionof sleeping site. González-Zamora et al . (2012) considered all latrines that were

located


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Ecological and Conservation Implications

This study supports the idea that sleeping sites are an important resource for spider

monkeys. They do not select sleeping sites at random, but use large trees from a small

subset of tree species, the architecture of which may provide them protection from predators and adverse climatic conditions (Aquino and Encarnación 1986; González-

Zamora et al . 2012; Reichard 1998). Also, sleeping sites are strategically located near

available food resources to increase foraging efficiency (Asensio et al . 2012a ;

Chapman 1989; González-Zamora et al . 2014; Mendes-Pontes and Lira 2005; Smith

et al . 2007; Teichroeb et al . 2012). In fact, sleeping sites showed a clumped distribu-

tion, and most were separated from one another by a distance


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